Applied Machine Learning Predoctoral Research Professional

The Center for Applied Artificial Intelligence is recruiting research professionals to tackle important and hard problems in economics and social science. Our research utilizes cutting edge technologies such as CNNs, GANs, and transformer models. We seek real world applications for these technologies, like reducing the discrimination against Black patients in healthcare algorithms and explaining drivers of bias in the US judicial system. Our research uses advanced technologies to develop our understanding of foundational elements of economics and social science, placing our work in the intersection of advanced technology with answering big questions about the world.

The research CAAI does

Our lab applies machine learning techniques to economics, health, social policy, and behavioral science to produce research insights as well as to make a positive difference in the world. The projects are diverse in scope and methods, from observational data to quasi-experimental methods to large-scale field experiments. You can read more about us and our research on the CAAI website.

Here are a few of the problems research professionals have worked on:

• Our recent paper in Science studied a widely-used health care algorithm that affects decisions on nearly a hundred million patients in the US. We found this algorithm had significant racial bias - Black patients were rated as lower risk than equally healthy White patients. We have now begun creating a fix for this algorithm and are working with systems to implement fixes.
• The covid-19 pandemic has shown us how important it is to get research insights into the hands of practitioners, policymakers, and the general public as quickly as possible. Like the example in this op-ed, we are working to build dashboards and tools to make research accessible to those who can use it to achieve impact.
• We are using a notoriously biased algorithm, facial recognition, for a good purpose. Human decision-makers can rely on faces, even when they ought not to. Using data collected by the CAAI, we train a face-algorithm that uncovers biases in how people make such choices.
  
  

The people you’ll work with

You’ll get the benefit of working closely with a faculty member on a project. We also have a community of pre-docs and research professionals that you can learn from. Finally, most of the lab’s projects involve collaboration with external faculty and you will get a chance to learn from them as well.

Here are some researchers we’ve worked with in the past or are currently working with:

• Jens Ludwig, UChicago Crime Lab
• Ziad Obermeyer, Berkeley School of Public Health
• Jon Kleinberg, Cornell University
• Kate Baicker, Harris School of Public Policy
• Jure Leskovec, Stanford University
• Mukund Sundararajan, Google
• Emma Pierson, Cornell Tech
• Himabindu Lakkaraju, Harvard Business School
• Devin Pope, Chicago Booth
• Oeindrila Dube, Harris School of Public Policy

The candidates we look for

To excel at interdisciplinary work, we require “bilingual” researchers. Firstly, successful researchers are required to master the latest computational tools. Secondly, successful researchers also need to discern the micro-econometric issues that arise with social science data. We are looking for candidates who are strong in one of the above areas, and committed to learning the other. In short, we partly consider this role to be an apprenticeship to learn the trade-craft of research.

Candidates should excel in one of these skills, and want to learn about the others:

• Empirical micro-econometrics – do you know what a regression discontinuity is or what LATE is? More importantly have you had experience in class (or ideally on some project work) applying these ideas and seeing the subtleties of social data?
• Computer science / data science – do you know what LDA is or have you built a pipeline that tackled a big hairy dataset?  Do you feel some expertise in either a single methodology (e.g. convolutional nets or generative models) or a single modality (such as images or language)?
  
  

In addition, candidates must:

• Have a bachelor’s or master’s degree in computer science, statistics, data science, economics or a closely related field required. Applicants must have either graduated from such a program, or intend to graduate by mid 2022.
• Have some interest in a PhD, enough to invest in or explore that interest, whether in economics, social science, data science, computer science, or another related field.
  
  

The contributions you’ll make

You’ll further our research program by contributing directly to ongoing research projects. Responsibilities include:

• Contributing to the design and implementation of an efficient and reproducible data processing pipeline.
• Building and rigorously evaluating statistical models using best practices of machine learning and statistical inference.
• Preparing project memos, summaries, presentations, reports, and other work products for dissemination to academic researchers, policymakers and other stakeholders, as needed.

The opportunities you’ll have

As part of your development as a researcher, you will enjoy being part of a community of scholars learning and pursuing research together, as well as:

• Joint lab meetings, so various research teams can collaborate and learn from each other.
• Interactive seminars with Booth and University faculty to investigate new research and possible challenges associated with the research trade.
• Academic advising to prepare you for your PhD career.
• Opportunities to connect with visiting experts and external collaborators.

Some formalities

Competencies:

• Advanced knowledge of data science techniques OR applied micro-econometrics OR theoretical statistics, math, physics or related field.
• Strong initiative and a resourceful approach to problem solving and learning required.
• Ability to work independently and as part of a team in a fast-paced environment required.
• Sound critical thinking skills required.
• Strong attention to detail with superb analytical and organization skills required.
• Excellent written and verbal communication skills, with the ability to present data in a simple and straightforward way for non-technical audiences required.

Education, Experience, and Certifications:

• Bachelor’s or master’s degree in computer science, statistics, data science, economics or a closely related field required. Applicants must have either graduated from such a program, or intend to graduate by mid 2022.

Technical Knowledge or Skills:

• Proficiency with statistical data analysis and machine learning using Python or R is required. The ability to work in both is preferred.

Apply now

Please apply to our job posting first, and then complete our opening assessment. Applications are currently rolling until July 14, 2022, and involve an assessment process and interview for promising candidates. Our assessment process requires candidates complete a small take-home puzzle set and a data challenge. The fellowship will begin in late Summer/early Autumn.

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.