Our web platform changes the way core people management questions are answered. With powerful, pre-designed analytics, your data fuels smarter workforce decision-making.
Identify micro-labor markets and target recruiting efforts in areas with untapped potential.
Current demographics, competitor wages, commuting patterns, and labor market conditions provide context at a hyper-local level.
Track attrition patterns over the employee lifecycle and identify the highest turnover periods in a typical career. Compare flight risk profiles across distinct workforce segments.
Give managers the predictive intelligence they need to target retention strategies.
Understand the drivers of performance to build a more productive workforce.
Score candidates in real time to fuel a data-driven hiring strategy.
Understand how current compensation policies manifest on the ground. Identify potential misalignments in pay and benefits.
Identify the role referrals are playing in your recruitment. Discover differences in quality across referrals so you can focus on the strongest networks.
Smart surveys provide structure to interviews, reducing bias and noise in crucial decision-making.
Understand the effect of a policy before implementing it. Quantify expected outcomes on your workforce and your bottom-line.
Collect the right information from your applicants, employees, interviewers, managers, and referrals to enable powerful predictive analytics.
At the heart of the labor market lies one central challenge: finding, building, and sustaining the right match between employee and employer. This pursuit underpins nearly all aspects of an employment relationship—from compensation and training to attraction and engagement. Yet in practice, companies approach these issues piecemeal, establishing best-practices largely through trial and error.
Three major developments, however, have set the stage for a new way to ask and answer the core questions in an employment relationship.
First, information systems have emerged to facilitate the systematic collection of electronic career and labor market data. Second, advances in statistics and processing power have given rise to powerful machine learning techniques, enabling the extraction of underlying patterns from large quantities of data. And finally, new theoretical frameworks—human capital investments, incentives, imperfect information, signaling, cognitive bias, and the fast versus slow brain—have fundamentally transformed our understanding of labor markets and the actors within them.
Each of these three pillars—information systems, pattern recognition, and theory—must come together to realize their transformative potential. Theories without data are academic. Data without information systems are limited. Patterns without theories are noise. The future of HR lies in harnessing these powerful synergies.
Our intellectual foundations are rooted in academic research and practical experience. This collection of case studies, white papers, and related academic articles is a way to share some of this knowledge.
Lalith Munasinghe, Kathryn Gautier (March 2018)
Most business leaders have heard about the huge potential of human capital analytics to create value, but how can they assess the potential for bottom-line impact? Using dozens of comprehensive simulations, our Impact tool enables HR leaders to understand the effects of policy changes before implementing new initiatives. And by quantifying financial impacts, it bridges the communication gap between CHROs and CFOs.
Lalith Munasinghe, Kathryn Gautier (March 2018)
At a US-based retailer, rising employee turnover rates had directors concerned about profitability. In addition to the obvious hard costs of turnover, they feared a growing proportion of rookies was starting to hurt store-level revenue. Through an analysis of their sales data in conjunction with the people data from their HRIS, they discovered that their fears were indeed justified. In fact, the relationship between job tenure and revenue was even more striking than they had imagined, and the insights launched a larger re-assessment of their talent retention management strategy.
Lalith Munasinghe, Kathryn Gautier (March 2018)
High turnover at a global financial services company was particularly concerning due to the extensive and costly four-week training program provided to all new hires. Smart Data rose to the occasion, building powerful predictive scores that could identify–during the application process–who would leave quickly, and who would stay. Yet the scores didn’t move the needle on turnover, leaving executives scratching their heads. This ultimately unsuccessful case study forms a reminder of the complex chain of technology, data, estimation, and end users that must come together to realize the value of predictive analytics.
Lalith Munasinghe, Kathryn Gautier (March 2018)
Retention efforts at a U.S. call center company was largely a manual process. As the labor market tightened, managers struggled to stay ahead of increasing turnover. Predictive employee flight risk scores became a systematic way of identifying their highest risk team members, so they could take action–before they quit. Providing managers with a targeted, actionable piece of information about their employees freed them from the never-ending catch-up game and empowered them to do what they do best–manage their people.
Lalith Munasinghe, Kathryn Gautier (July 2017)
The accepted wisdom of the day is that employee referrals bring in higher quality applicants and hires. Yet most practitioners don’t know why. This gap in understanding means that many companies have not captured the huge potential trapped in their employees’ networks. This white paper offers a practical introduction to the powerful mechanism behind employee referrals, providing the insight needed to unleash this potential.
Lalith Munasinghe, Kathryn Gautier (July 2017)
Companies want to hire high productivity people. But how do we define productivity? How do we measure it? Chances are that different companies, even various people within the same company, will have opposing answers to these questions. Unfortunately, in the absence of clear metrics, there is little hope of modeling productivity to support hiring managers in selecting the right people. This white paper explores whether employment duration can be used as a proxy for productivity in order to unlock the potential of predictive analytics. We use evidence from a large customer services company to test the age-old intuition that bad matches don’t last.
Lalith Munasinghe, Cynthia Howells (October 2016)
Turnover, attrition, and tenure are all commonly used buzzwords in human resources and business operations. Phrases like “the costs of high turnover” or “shorter average tenure” are certainly familiar in the corporate vernacular. Yet these terms are often used sloppily, revealing fundamental misunderstandings of labor turnover. These misunderstandings can result in confusing reports, nonsensical analytics, and even serious strategic errors in people management. To make sense of them we must return to the underlying concept–the duration of an employment relationship. This white paper aims to bring clarity to these terms.
Lalith Munasinghe, Cynthia Howells (August 2016)
In a brief introduction to the value of employee referrals, this paper uses data from a large call center company to assess differences between hires who came through internal referrals versus traditional channels.
Lalith Munasinghe, Rena Rosenberg, Cynthia Howells (June 2016)
Senior executives quantify thousands of costs in their organizations, yet most lack a rigorous methodology for calculating the costs associated with employee turnover. How can leaders manage what they can’t measure? This paper introduces the two distinct components in turnover costs–(1) hard costs of hiring and training and (2) top-line losses due to reduced productivity–providing executives with the rigorous financial framework they’re missing.
Forthcoming in the Journal of Labor Economics (Barr, Bojilov, Munasinghe, 2019)
ABSTRACT. Referrals can impact employment outcomes at various stages of the hiring process. We develop a model to estimate the role of referral information on job offers, acceptances, turnover, and performance. Using rich data from a call center company, we show that referrals generate a superior pool of applicants. Estimates from our multi-stage hiring model reveal that referrals induce positive sorting at the job o¤er stage on hard-to-observe information about performance and thus referred applicants complete much of the sorting during the hiring process.
Working Paper (Carle 2018)
Over the past decade, machine learning algorithms have reshaped information exchange, prediction, and decision-making. Because of their flexible framework, machine learning techniques have proved widely successful in a number of applications ranging from translation, to medical diagnoses, and résumé screening. However, concerns about bias, privacy, and the overall effectiveness of machine learning algorithms have materialized as well. Academics have uncovered “algorithmic bias” in models used in judicial decision-making, hiring, and targeted advertising. This paper argues that the data used to build machine learning algorithms is the foremost determinant of their predictive power and capacity for mitigating human biases. Thus, it is not algorithmic bias, but data biases that should be of primary concern. This conclusion motivates further research in techniques to generate richer data, alleviate biases in existing data, and produce better-defined guidelines for how the technology should fit into decision-making processes.
Working Paper (Gautier 2016)
ABSTRACT. This paper explores the interrelationship between wages, turnover, and performance using payroll and employment data from a call center company. I study how various compensation components evolve over time and how they are related to turnover. I find that the monthly performance-based component of compensation is negatively related to turnover, suggesting that it can be interpreted as reflecting match-specific compensation, broadly defined. I also explore the differences of this match-specific component between referred and non-referred employees. I find that referred employees earn more in the match-specific component of compensation, and that this drives their lower turnover rates. Consistent with literature, this suggests that referred employees are better matched than their non-referred counterparts.
Journal of Labor Economics (Hensvik, Skans, 2016)
ABSTRACT. The Montgomery (1991) model of employee referrals suggests that it is optimal for firms to select new employees through referrals from their most productive workers, as they are likely to know others with high unobserved productivity. In this paper, we use rich matched employer-employee data with cognitive and non-cognitive test scores to assess the model's ability to explain why firms recruit former coworkers of incumbent employees. Our empirical results support key elements of the model: incumbent workers of high aptitude are more likely to be linked to entering workers.
Review of Economic Dynamics (Galenianos, 2016)
ABSTRACT. The firm’s decision to use referrals as a hiring method is studied in a theoretical model of the labor market. The labor market is characterized by search frictions and uncertain quality of the match between a worker and a job. Using referrals increases the arrival rate of applicants and provides more accurate signals regarding a worker’s suitability for the job. Consistent with the data, referred workers are predicted to have higher wages, higher productivity, and lower separation rates and these differentials decline with tenure.
Working Paper (Wollburg, 2016)
Market intermediaries are individuals, organizations, or platforms that facilitate transactions between two or more parties, performing “work that otherwise would be performed by the provider or consumer of a good.” Typically, market intermediaries assist with or execute one or more of three essential market functions: (1) searching: the gathering of information relevant for the transaction, (2) matching: bringing together parties compatible as transaction partners, and (3) transaction: the negotiation of the transaction and finalizing of the transaction agreement.
Review of Economic Studies (Kramarz, Skans, 2015)
ABSTRACT. The paper studies the importance of family networks and the way these networks affect the transition from school to work. We use a Swedish population-wide linked employer-employee data set that also includes detailed information on family ties, schools, and class composition. Analyzing parental characteristics (wage and seniority, in particular), job characteristics (type of occupation, wage, stability), the economic environment (unemployment, job structure in the municipality) at hiring, as well as employing firm characteristics, we find that family networks reduce the uncertainty inherent in the transition between school and work and that children in “weak” positions tend to get their first jobs with a little help from their “strong” parents.
Quarterly Journal of Economics (Burks, Cowgill, Hoffman, Housman 2015)
ABSTRACT. Using personnel data from nine large firms in three industries (call centers, trucking, and high-tech), we empirically assess the benefit to firms of hiring through employee referrals. Compared to nonreferred applicants, referred applicants are more likely to be hired and more likely to accept offers, even though referrals and nonreferrals have similar skill characteristics. In call centers and trucking, the two industries for which we can calculate worker-level profits, referred workers yield substantially higher profits per worker than nonreferred workers. These profit differences are driven by lower turnover and lower recruiting costs for referrals.
Federal Reserve Staff Report (Brown, Setren, Topa 2013)
ABSTRACT. The limited nature of data on employment referrals in large business and household surveys has so far limited our understanding of the relationships among employment referrals, match quality, wage trajectories, and turnover. Using a new, firm-level data, we are able to provide rich detail on these empirical relationships for a single U.S. corporation. Predictions with which our results align include: 1) referred candidates are more likely to be hired, 2) referred workers experience an initial wage advantage, 3) the wage advantage dissipates over time, 4) referred workers have longer tenure in the firm, and 5) the variances of the referred and nonreferred wage distributions converge over time.
Penn State University (Galenianos, 2012)
ABSTRACT. An equilibrium search model of thelabor market is combined with a social network. The key features are that the workers’ network transmits information about jobs and that wages and firm entry are determined endogenously. Empirically, the inter-industry variation in aggregate matching efficiency is attributed to variation in referral use. The model predicts that the efficiency of the aggregate matching function is pro-cyclical, which is consistent with empirical evidence.
Review of Economic Dynamics (Oyer, Schaefer 2012)
ABSTRACT. We study the sources of match-specific value at large American law firms by analyzing how graduates of law schools group into law firms. We measure the degree to which lawyers from certain schools concentrate within firms and then analyze how this agglomeration can be explained by "natural advantage factors" (such as geographic proximity) and by productive spillovers across graduates of a given school. We show that large law firms tend to be concentrated with regard to the law schools they hire from and that individual offices within these firms are substantially more concentrated.
University of Washington (Heath, 2011)
ABSTRACT. This paper argues that firms use referrals from current workers to mitigate a moral hazard problem. I develop a model in which referrals relax a limited liability constraint by allowing the firm to punish both the referral recipient and referral provider if the recipient has low output. This punishment implies that there is positive correlation between the provider’s and recipient’s wages at a given time and that the wage variance of providers is higher than that of observably similar non-providers. The model also predicts that providers are observably higher-skilled than other workers, since their wages are higher relative to a fixed limited liability constraint.
University College London (Dustmann, Glitz, Schönberg 2011)
ABSTRACT. This paper develops a model and derives novel testable implications of referral-based job search networks in which employees provide employers with information about potential job market candidates that they otherwise would not have. Using unique matched employer-employee data that cover the entire workforce in one large metropolitan labor market over a 20 year period, we find strong support for the predictions of our model. We first show that firms are more likely to hire minority workers from a particular group if the existing share of workers from that group employed in the firm is higher. We then provide evidence that workers earn higher wages, and are less likely to leave their firms, if they were hired by a firm with a larger share of minority workers from their own group and are therefore more likely to have obtained the job through a referral.
Working Paper (Jung, Munasinghe 2010)
ABSTRACT. We present theory and evidence on the effects of wage volatility on labor mobility. Our model of job turnover explicitly incorporates variance of within-job wages by assuming that wages evolve as random walk processes. With the additional assumption that job changes entail “switching” costs, the key theoretical result is that the optimal threshold of turnover—the minimum wage difference between outside and inside jobs necessary for a job change—is positively related to wage volatility. Data from the National Longitudinal Surveys of Youth show that workers who hold more volatile jobs get bigger wage gains when they quit and move to a new job and that they quit less frequently especially if their jobs are also characterized by high within-job wage growth rates. These findings are consistent with the implications of our theoretical model.
Labour Economics (Munasinghe, Reif, Henriques, 2007)
ABSTRACT. We present empirical evidence on gender differences in wage returns on firm-specific experience (job tenure) and general experience. We find that overall returns on an extra year of labor market experience are lower for women than men. However, a decomposition analysis shows that the return on job tenure is substantially lower for women than it is for men, and that the return on general experience is higher for women than it is for men. These findings are consistent with the hypothesis that despite their growing attachment to the labor market, women are less likely to invest in job-specific skills or to self-select into jobs with backloaded compensation because women are more prone to job separations than their male counterparts.
Working Paper (Munasinghe 2006)
ABSTRACT. The paper presents a theory of compensation— built on search and matching, firm-specific human capital, and self-enforcing wage contracts—that provides a unified explanation for a broad range of empirical observations on wage and turnover dynamics. For example, the model resolves the apparent puzzle posed by the lack of evidence of wage growth heterogeneity among jobs despite the fact that the same data show past wage growth on the job reduces turnover. The key implications of the model are as follows. First, wages increase and turnover rates decrease over the duration of an employment relationship, but the positive tenure effect on wages is predicted to be quantitatively weaker than the negative tenure effect on turnover. Second, within-job wage growth is higher and turnover is lower in high productivity growth jobs than in low productivity growth jobs. Third, serial correlation of within-job wage increases is indeterminate in spite of the assumed serial correlation of the underlying productivity increases on the job.
Eastern Economic Journal (Munasinghe, Sicherman 2006)
ABSTRACT. The title of our paper “Why Do Dancers Smoke?” suggests a paradox. Dancers place great importance on physical health, strength, and fitness; and yet, smoking leads to untoward health, loss of strength, and diminished fitness. We contend that the concept of time preference, or, in the economic parlance, of individual discount rates–i.e. the variation in individual valuations of present versus future consumption–resolves this apparent paradox. Both activities sacrifi ce some distant benefit for a more present-oriented gratification. Dancers are passionate, if not obsessed, with their work; but their careers are short with dim, if not non-existent, prospects of future earnings. Even more obvious is the fact that smokers sacrifice future health for an immediate source of pleasure. Hence the answer we consider is that dancers smoke because they are more present-oriented.
Journal of Labor Economics (Munasinghe, O'Flaherty 2005)
ABSTRACT. Turnover falls with tenure, but wages do not always rise (and sometimes fall) with tenure. We reconcile these findings by revisiting an old issue: how gains from firm-specific training are split between workers and firms. The division is determined by a stationary distribution of outside offers. The lower the wage a firm pays to a specifically trained worker, the more profit it makes but the more likely the employee is to leave. The optimal time paths of wages and turnover show that, if marginal product is increasing, wages need not be increasing but it always implies a falling turnover rate.
Labour Economics (Munasinghe, 2005)
ABSTRACT. This paper presents evidence on the effects of worker expectations on labor turnover, a topic largely ignored in the voluminous literature on labor mobility. Two survey instruments related to expected job duration and chances of promotion in the National Longitudinal Surveys of Youth are used to analyze the role of job prospects in predicting turnover dynamics. The key empirical finding is that workers with favorable job assessments have a lower and flatter tenure-turnover profile—i.e. the well-known negative structural relationship between the turnover rate and job tenure-than their counterparts with less favorable job assessments. This finding is consistent with search and matching theories that explicitly incorporate heterogeneity of prior beliefs about match quality.
American Journal of Sociology (Castilla, 2005)
ABSTRACT. Much research in sociology and labor economics studies proxies for productivity; consequently, little is known about the relationship between personal contacts and worker performance. This study addresses, for the first time, the role of referral contacts on workers’ performance. Using employees’ hiring and performance data in a call center, the author examines the performance implications over time of hiring new workers via employee referrals. This study finds that referrals are initially more productive than nonreferrals, but longitudinal analyses emphasize posthire social processes among socially connected employees.
Journal of Economic Behavior & Organization (Tassier, Menczer, 2005)
ABSTRACT. We construct a model of referral hiring to examine the effects of social network structure on group level inequality. Our study departs from many studies of social networks and labor market outcomes in that we focus on groups and not on individuals. We find that more random social networks yield higher employment rates than less random social networks if the population is integrated or information flows about job vacancies are random. However if the population is highly segregated and information flows about job vacancies are non-random then less random social networks have higher employment rates than more random social networks.
University of Connecticut (Bayer, Ross, Topa, 2004)
ABSTRACT. We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than individuals in nearby but not identical blocks. We find significant evidence that social interactions operating at the block level (residing on the same versus nearby blocks) increases the probability of working together by over 33 percent. We provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.
Labour Economics (Munasinghe, Sigman 2003)
ABSTRACT. We present an analysis of labor mobility as a predictor of wages and job turnover. Data from the National Longitudinal Surveys of Youth show that workers with a history of less frequent job changes (stayers) earn higher wages and change jobs less frequently in the future than their more mobile counterparts (movers). These mobility effects on wages and turnover are stronger among more experienced workers, are highly robust across various model specifications, and persist despite corrections for unobserved individual fixed effects. In the second half of the paper we present a simple two period stochastic model of job mobility to study wages across movers and stayers.
Quarterly Journal of Economics (Altonji, Pierret 2001)
ABSTRACT. We show that if firms statistically discriminate among young workers on the basis of easily observable characteristics such as education, then as firms learn about productivity, the coefficients on the easily observed variables should fall, and the coefficients on hard-to-observe correlates of productivity should rise. We find support for this proposition using NLSY79 data on education, the AFQT test, father's education, and wages for young men and their siblings. We find little evidence for statistical discrimination in wages on the basis of race. Our analysis has a wide range of applications in the labor market and elsewhere.
Journal of Political Economy (Munasinghe, O'Flaherty, Danninger, 2001)
ABSTRACT. The past century and a quarter has seen frequent improvements intrack and field records. We attempt to estimate what proportion of the speed of record breaking is due to globalization (competitors from more countries) and what proportion is due to technological progress (better equipment and training techniques). It appears that technological change is the chief driving force but that technological progress is improving the performance of seasoned elite athletes faster than it is improving the performance of adolescents. Both our results and our methods may have wider application.
Social Science Research (Elliott, 2001)
ABSTRACT. Research on organizations and labor markets has rekindled interest in the role of insider referrals in matching workers to jobs, emphasizing the contribution this process makes to the reproduction of ethnic segregation in local labor markets. Three key findings emerged. First, evidence showed that insider referrals account for nearly all ethnic/immigrant variation in informal job matching. Second, Latinos, especially newly arrived immigrants, are more likely than native-born Whites to enter jobs through insider referrals. Third, the correlation between insider referrals and ethnically homogeneous jobs is positive and significant only for native-born Blacks.
Journal of Labor Economics (Munasinghe, 2000)
ABSTRACT. Theories of turnover and wage dynamics have studied the impact of wage levels on turnover, but they have failed explicitly to model the role of wage growth in predicting turnover. This article presents a theory of turnover that explains why within-job wage growth reduces the likelihood of worker-firm separations. The model determines the evolution of value among jobs that differ systematically in permanent rates of wage growth and shows that the value of high wage-growth jobs increases faster. With additional assumptions about the search process, this proposition implies that high wage-growth jobs are less likely to end.
American Journal of Sociology (Fernandez, Castilla, Moore, 2000)
ABSTRACT. This article argues that a common organizational practice-the hiring of new workers via employee referrals-provides key insights into the notion of social capital. Employers who use such hiring methods are quintessential "social capitalists," viewing workers' social connections as resources in which they can invest in order to gain economic returns in the form of better hiring outcomes. Using unique company data on the dollar costs of screening, hiring, and training, this article finds that the firm's investment in the social capital of its employees yields significant economic returns.
American Sociological Review (Fernandez, Weinberg, 1997)
ABSTRACT. Using unique data from a large retail bank, we investigate the theoretical mechanisms by which preexisting social ties affect the hiring process. We study how employee referral (i.e., being recommended by a current bank employee) affects an applicant's success at multiple stages of the recruitment process, and we examine the cumulative effects of referral status on the chance of being offered a job. Results of probit models indicate that, controlling for other factors, referrals have advantages at both the interview and job-offer stages compared to external nonreferral applicants.
Journal of Labor Economics (Simon, Warner, 1992)
ABSTRACT. Firms often view job applicant referrals from current employees as more informative than direct applications or referrals through formal labor market intermediaries such as placement firms. We argue that old boy networks reduce employers' uncertainty about worker productivity. Using Jovanovic's job matching model, we show that workers hired through the old boy network should (1) earn higher initial salaries, (2) experience lower subsequent wage growth on the job, and (3) stay on the job longer than otherwise comparable workers hired from outside the network. We find considerable support for this theory using data from the 1972 Survey of Natural and Social Scientists and Engineers.
The American Economic Review (Montgomery, 1991)
ABSTRACT. Labor economists have long recognized that many workers find jobs through friends and relatives; personnel researchers argue that employee referrals are a useful device for screening job applicants. Because the use of employee referrals is both widespread and purposive, social structure—the pattern of social ties between individuals—may play an important role in determining labormarket outcomes. In this paper, I attempt to embed social structure in a stylized economic model of the labor market.
Our team is a collection of our founder's top students, collaborators, and co-authors. Coming from a wide range of educational and professional backgrounds, we share a common vision for the future of people analytics.
Lalith is the founder of Talenteck and a Professor of Economics at Barnard College and the School of International and Public Affairs at Columbia University in New York City. Educated in Philosophy and Economics at Princeton, Cambridge and Columbia, he has since pursued a dual career in academia and industry.
While his research and work experience have been expansive, two questions have remained central, forming a consistent conceptual thread through all his work: What constitutes a good match between an employee and a company? and What is the role of human capital investments in our conception of freedom?
Lalith’s research has focused on employment outcomes: productivity, compensation, and turnover. He has established theoretical and empirical links between these key outcomes and worker characteristics such as experience, training, mobility, expectations, networks, time preferences, and gender. In a study of record-breaks using track and field data, he also developed novel methodology for modeling discrete outcomes, applicable in the labor market, climate change, and beyond. His most recent work has explored the role of referrers and interviewers in today’s workplace. His publications have appeared in the Journal of Labor Economics, Journal of Political Economy, and Labour Economics, among other academic journals.
In addition to studying company data in an academic environment, Lalith also collaborated with companies to translate his research into tangible business outcomes. He worked on several consulting projects in human capital strategy with Mitchell Madison Inc., William M. Mercer Inc., IntelliRisk Management Corp., and the World Bank. He was also the Head of Analytics at iQor Inc., a financial services company. There, he leveraged technology to build and implement a suite of human capital analytic products.
Over his decades-long career, Lalith has come to view theory as an integral part of effective analytics, and particularly, in human capital analytics. His perspective is that these powerful abstract ideas are what enable us to ask the right questions, build scalable solutions, and ultimately create impact. In 2014, Lalith founded Talenteck, in many ways a culmination of these ideas.
As the head of product development, Kate is dedicated to creating an extensive, scalable analytics platform that provides actionable intelligence for our client base. She is integral in solving some of the toughest analytic problems we face, using her mathematical training in abstraction, recursion, and conceptual isomorphisms. She also synthesizes expertise from our team of top academic researchers, technologists, and design architects. Her experience collaborating directly with clients across diverse industries—including retail, manufacturing, BPO, consulting, and software engineering—informs her work, ensuring solutions remain tied to real business problems.
Kate graduated summa cum laude and as a member of Phi Beta Kappa with a B.A. in Mathematics from Barnard College, Columbia University. Her studies also included advanced coursework in Economics and Machine Learning.
As the head of technology and database management, Sakthi is responsible for the development, security, and integrity of our technology infrastructure. She plays a key architecting role in the product and manages the software development team. Her extensive experience in diverse technology stacks and commitment to quality inform her work. Prior to joining TalenTeck, she worked at Cognizant.
Sakthi earned her B.Tech in Information Technology from Vellore Institute of Technology.
As a key contributor to product development, Ellorine focuses on Smart Data collection technologies. Her excellent writing skills, developed in part during her time as a staff writer for the Columbia Spectator, are invaluable in product development, communications, and several other areas. She also manages client onboarding, working closely with client technology and data teams.
Ellorine graduated cum laude from Barnard College, Columbia University with a B.A. in Economics. Her studies also included independent research in bias and transparency in machine learning algorithms.
Working in product development, Calvin is dedicated to a strong marriage of form and function. He brings an eye for design, coupled with an understanding and deep respect for the conceptual foundations and functionality of the product.
Calvin is currently pursuing a B.A. in Computer Science and Mathematics at Columbia College, Columbia University.
With a focus on Labor Economics, Stephen’s research contributes to the intellectual foundations for our product. He is most noted for his econometric and applied work on educational selection and the dynamics of educational attainment. His published work has appeared in the Journal of Political Economy, Journal of Labor Economics, and American Economic Review, among others.
He spent many years as an Associate Professor at Columbia University, and is currently an Adjunct Associate Professor at the School of International and Public Affairs at Columbia University.
Stephen obtained his B.A. in Economics from Brigham Young University, and his Ph.D. in Economics from the University of Chicago, where he studied under Nobel Laureate James Heckman.
As an economist, Tack’s research contributes to the intellectual foundations for our product. His topics of primary interest are networks and climate change. His published work has appeared in the Journal of Climate, Journal of Evolutionary Economics, and Economic Theory, among others. He also co-authored a paper with founder Lalith Munasinghe on climate change.
Tack was a key member of the product development team during a sabbatical year in the United States. He is currently a Professor at Kyung Hee University in South Korea.
Tack earned his B.A. in Economics from Kyung Hee University, and his Ph.D. from Columbia University.
With a focus on Labor Economics and Econometrics, Tavis’s research contributes to the intellectual foundations of our product. His topics of primary interest are cohort analysis, referral networks, and the role of interviews. His published work has appeared in the Journal of Labor Economics and Journal of Macroeconomics, among others. He also co-authored a paper with founder Lalith Munasinghe on the role of referrals in labor market matchmaking.
Tavis was also a key member of the product development team during TalenTeck’s first two years, building technology infrastructure and contributing rich analytic content. Prior to joining TalenTeck, he taught at Beijing Normal University and School of International and Public Affairs at Columbia University.
Tavis obtained his B.A. in Computer Science and an M.A. in Economics from Boston University, and a Ph.D. in Economics from Columbia University.
With a focus on Econometrics and Labor Economics, Raicho’s research contributes to the intellectual foundations for our product. His topics of primary interest are matching, incentives, and interviews. His published work has appeared in the Journal of Labor Economics, among others, and he co-authored a paper with founder Lalith Munasinghe on the role of referrals in labor market matchmaking.
He is an Assistant Professor at Ecole Polytechnique in Paris.
Raicho obtained his B.A in Economics and Political Science from Grinnell College, and his Ph.D. in Economics from Columbia University.
Karl’s research in stochastic modeling and queuing theory contributes to the intellectual foundations for our product. He uses probability theory, random process theory, and simulation theory to model and analyze systems in which randomness is inherent, such as in telecommunications systems, queueing systems, inventory systems, and insurance risk businesses. His published work has appeared in Operations Research and the Journal of Applied Probability, among several others. He also co-authored a paper with founder Lalith Munasinghe on mobility in the labor market, published in Labour Economics.
He is the current Director of Columbia University’s Center for Applied Probability and the Director of Undergraduate Programs for the Department of Industrial Engineering and Operations Research.
Karl received a B.A. in Pure Mathematics from the University of California, Santa Cruz in 1980, and then an M.A. in Mathematics and an M.S. and Ph.D. in Industrial Engineering and Operations Research, all from the University of California, Berkeley.
As a labor economist, Brendan’s research contributes to the intellectual foundations for our product. His research explores the labor market impacts of technology and trade, the consequences of job loss, and the efficacy of public policies designed to help displaced workers and distressed areas recover from adverse changes in labor demand. His published work so far has appeared in the Journal of Labor Economics, and American Review of Economics Papers & Proceedings.
He is an Assistant Professor at the University of California, Davis.
Brendan graduated summa cum laude and as a member of Phi Beta Kappa with a B.A. in Economics and Political Science from Columbia University. He earned his Ph.D. in Economics from the Massachusetts Institute of Technology, where he was a co-winner of the first-place prize for the W.E. Upjohn Institute’s Dissertation Award.
As an advisor, Sonya brings a wealth of expertise and experience in business strategy and corporate development.
Sonya has been serving as C.E.O. and President of Carl Fischer Music & Theodore Presser Company since 2006. Here, she played an integral role in combining operations with Theodore Presser Company, including the development and installation of a comprehensive software package for both companies. Prior to joining Carl Fischer Music & Theodore Presser Company, she spent three years in New York working with founder Lalith Munasinghe at the Human Capital Strategy Practice at Mercer Consulting before moving to California for a brief foray into the world of software development as the Senior Manager of Strategic Planning at Struxicon.
Sonya earned her B.A. in Economics and Music at Barnard College, Columbia University and Julliard School, through the Columbia-Juilliard Joint Program, and an M.B.A. from Harvard Business School
As an advisor, Rena brings a wealth of expertise and experience in entrepreneurial thought leadership and business strategy, particularly in growth strategy.
Rena was a partner at McKinsey & Company from 2010 to 2016, where she founded a digital service line for the Pharmaceuticals and Medical Device Practice, and directed brand strategy design. As a partner at Robin Hood Ventures, she invests in early stage, high-growth life sciences and technology companies, and advises seed and growth-stage organizations on growth strategy, product and business development, and sales and marketing efforts. She is currently an Adjunct Professor at Barnard College, Columbia University, where she teaches economics of healthcare.
Rena graduated magna cum laude with a B.A. in Economics from Barnard College, Columbia University. She earned her Ph.D. in Economics from the University of Chicago.
Cynthia brings a wealth of experience in finance and communications to her role as an advisor.
Cynthia served as Director of Project Finance at Fitch Ratings from 2009 to 2013. Prior, she was a Vice President at the Bank of New York. During her 17 years as a corporate finance executive, she provided project finance ratings and corporate banking analysis to renewable and traditional electric companies. She has unique expertise in identifying business strength and weaknesses, as well as exceptional writing skills. Before entering finance, she served as Editor of the Columbia Journal of World Business.
Cynthia earned her B.A. in English Literature from Columbia College, Columbia University, and her M.A. in Banking and Finance from the School of International and Public Affairs at Columbia University.
As an advisor, Sanjay brings a unique perspective with his extensive experience in Finance.
Sanjay is senior advisor to KAUST Investment Management Co. Previously, he was managing director for alternative-investment solutions within UBS Global Asset Management A&Q, where he served on the global investment committee in addition to heading the London, Hong Kong and Tokyo investment offices of the group. He is currently a visiting professor at the University of Hong Kong School of Economics and Finance.
Sanjay graduated from Princeton University with a B.A. in Computer Science, and was a doctoral candidate and teaching fellow in the Philosophy program at Harvard University.
As an advisor, Matt brings extensive experience in human capital analytics.
Matt currently leads Edelman Intelligence’s Business Analytics and Science offering, which leverages advanced analytics to link clients’ paid, owned, and earned environments to business outcomes. Prior to joining Edelman, he was a partner at Accenture, where he led their Advanced Analytics and Digital practices. He also worked with founder Lalith Munasinghe at the Human Capital Strategy Practice at Mercer Consulting, and on the analytics team at iQor.
Matt earned his B.S. in Economics and Finance from the University of Illinois, M.A. from Johns Hopkins University and was a former Ph.D. candidate at Georgetown University.
As our legal advisor, Greg brings extensive experience and expertise in corporate law, with a focus on compliance.
He has served as Chief Compliance Officer and Executive Vice President at iQor since 2005. Previously, he was engaged in private practice as a partner in a large New England-based law firm where he focused on corporate and transactional matters.
Greg earned his B.A. in Economics from Fairfield University and his J.D. from John Marshall Law School.