Becker Friedman Institute - Development Innovation Lab
About the Department
The Development Innovation Lab uses the tools of economics to develop innovations with the potential to benefit millions of people in low- and middle-income countries. You can read more about DIL's research and team at https://bfi.uchicago.edu/development-innovation-lab/.
The Development Innovation lab is seeking an Analytics Lead to support our portfolio of large-scale research and policy projects related to child health, agriculture, education, and COVID-19. The Analytics Lead will offer technical support and advisory services across our projects on research methods and data analysis, and also will be responsible for institutional development and training on data best practices across the lab.
An ideal candidate will have experience applying a variety of statistical modeling approaches to large datasets in real-world settings. Our Analytics Lead will support in all areas of data collection, cleaning, model development and implementation. The position offers the opportunity to work directly with leading researchers and policymakers on projects with immediate real-world impact. You will collaborate closely with PhD-level economics, as well as a team of Research Professionals, Research Managers, Postdocs, and Directors.
- Provides advice and technical support across teams of academics, research staff, and implementing partners.
- Leads data analysis and management on research projects by supporting cleaning, merging, matching, sampling, modeling, and randomizing data.
- Serves as an in-house expert for key datasets, and advises research staff to ensure consistent use of data.
- Provides consultation to staff on advanced statistical techniques such as randomization, power calculations, data transformations, econometrics, etc.
- Establishes training, onboarding and monitoring systems for data and coding practices.
- Contributes individually to new methods development.
- Leads projects related to building and maintaining best data practices across DIL. This may include quality assurance processes, creating code standards, and building templates and tools for data management and visualization.
- Works with the DIL leadership team on issues of organizational development and leadership, potentially including identifying areas for staff development and professional development related to data best practices and building policies within the lab.
- May supervise other research staff and provide guidance on professional development, goal setting, and lab- or organization-wide opportunities.
- Has a deep understanding of methods to analyze complex data sets for the purpose of extracting and purposefully using applicable information. May develop and maintain infrastructure that connects data sets.
- Calibrates data between large and complex research and administrative datasets. Guides and may set the operational protocols for collecting and analyzing information from the University's various internal data systems as well as from external sources.
- Designs and evaluates statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and statistical inference. Provides expertise for high level or complex data-related requests and engages other IT resources as needed. Partners with other campus teams to assist faculty with data science related needs.
- Performs other related work as needed.
Minimum requirements include a college or university degree in related field.---
Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.---
- Bachelor's degree in economics, public policy, statistics, computer science or closely related field.
- Advanced degree.
Technical Skills or Knowledge:
- Working with field experiments and randomized control trials.
- Experience managing analytics staff.
- Experience working with large, complex datasets.
- Experience working with R,Python, Stata, or other programming languages.
- Program evaluation methods experience (difference-in differences, propensity score matching, RD).
- Work in Git and familiarity with Github.
- Strong knowledge of randomized controlled trials and other empirical methods used in economics.
- Strong interest in development and social policy.
- Solid quantitative skills.
- Effective written and verbal communication skills.
- Strong data visualization ability.
- Excellent time management and organizational skills including the ability to meet external and internal deadlines.
- Strong coordination and management skills.
- Resume/CV (required)
- Cover Letter (required)
- Three Professional References Contact Information (required)
When applying, the document(s) MUST
be uploaded via the My Experience
page, in the section titled Application Documents
of the application.Job Family
Individual ContributorFLSA Status
MonthlyScheduled Weekly Hours
YesRequires Compliance with University Covid-19 Vaccination Requirement
YesDrug Test Required
NoHealth Screen Required
NoMotor Vehicle Record Inquiry Required
Employees must comply with the University's COVID-19 vaccination requirements. More information about the requirements can be found on the University of Chicago Vaccination GoForward.
The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national or ethnic origin, age, status as an individual with a disability, protected veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.
Staff Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.
We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages a diversity of perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange.
All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.
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