Oversee implementation of new databases and use of dbt to support data science projects.Manage the data science team, including full time data scientists as well as part time project assistants and student interns. Oversee analyses requested by university leadership to support decision-making including: Designing a regression-discontinuity analysis to infer causal impact of new student intervention policies; Developing a new faculty hiring recommendation model which uses equitable measures to predict success in research; Constructing interactive dashboards for university stakeholders to easily grasp trends and patterns in student and faculty success.

Work with data warehouse and integrated research computing teams to establish a technology stack that can support a data science team.Conducted analyses requested by university leadership to support decision-making, including: using generalized synthetic control to evaluate the causal impact of a particular student recruitment policy, which helped determine whether there was value in continuing the policy; using regularized logistic regression and xgboost to predict student outcomes, allowing the school to identify students in need of intervention and accommodations; using time-series forecasting to predict total research grants, which helped inform faculty hiring decisions.Communicate findings by developing dashboards and interactive reports and by presenting in-person to university leadership.Supervise project assistants and interns to provide them opportunities to grow their skills while also adding value to our office.

Tutoring undergraduate and graduate level students in mathematics and economics.

• Used Julia for structural modeling and parameter calibration with real economic data to demonstrate the risk of large model errors under typical assumptions.• Analyzed patterns in real data on consumption and retirement in order to derive a new model of dynamic labor preferences. Used dynamic programming numerical model solution techniques and GMM estimation to show my model could reconcile previously conflicting empirical findings.• Used SQL, Python, R, SAS, STATA, Julia, Matlab for joining data from multiple sources, data cleaning, analysis, statistical inference model building, numerical model solving, and visualizing data.• Wrote shell scripts (Linux) to implement large distributed and multithreaded jobs on university super computer, allowing for computations that were infeasible on a personal machine.

Designed Curriculum, Lectured, and Graded Course

• Used Stata and R for data cleaning, exploration, analysis to summarize facts used to motivate academic research.• Contributed to collective codebase and introduced team to git and github for collaboration and version control.• Used regression analysis in Stata to identify career patterns in ACS data which helped lead to project approval for use of Census Bureau Restricted LEHD micro data set.• Implemented hypothesis testing and panel regression methods including two-way fixed effects on large economic data set to reveal novel patterns in the relationship between college major, wage growth, and job mobility.• Used data visualization packages in Stata, Julia, and Python to creative intuitive and interactive displays of results.• Above activities contributed to working papers “Returns to College Majors,” Pavan, “Who Are the Hand-to-Mouth” Aguiar, Bils, Boar.

Taught review classes, tutored, and graded coursework for: Financial Math, Microeconomics (intermediate and advanced), International economics, Monetary Economics and Banking

Coded VBA scripts for Excel and Outlook that: improved and automated creation and presentation of 401k performance reports; identified and correct data errors during database transfer; summarized and presented data on client meetings, which allowed the team to better organize its calendar.

• Explored alternative assumptions on utility• Developed the foundations of a microeconomic model• Performed Model analysis and representation with MatLab• Received grant funding for project

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