Research Director

Department
 

UL Urban Crime Lab

About the Department
 

The Crime Lab and its sister organization, the Education Lab, is a non-partisan faculty-led research center at the University of Chicago directed by professors Jens Ludwig and Jonathan Guryan. Our mission is to help design, test, and scale solutions to some of our nation’s most pressing and persistent social problems. We use the power of research and data science to help public sector agencies improve social conditions for the people they serve. Our focus on partnering with the public sector is motivated by the recognition that only the government has the scale of operations to translate promising solutions into large-scale social change. Our work seeks to have impact in the world through two key pathways. The first pathway to impact is directly through the design and testing of promising policy levers in conjunction with our government and NGO partners. Past projects have, for example, helped the city of Chicago re-orient much of its violence strategy towards greater use of social programs, informed the Chicago Public Schools decision to allocate millions of dollars to intensive high-dosage tutoring for children growing up in some of the city’s most economically disadvantaged communities, supported the implementation of reforms at the Chicago Police Department initiated by the Obama Administration’s Department of Justice civil rights division, and worked with the New York City’s mayor’s office on the effort to close the long-troubled Rikers Island jail without compromising public safety in the city. The second pathway to impact is by drawing generalizable lessons from these local R&D initiatives that are intended to inform scientific and policy efforts across the country, or even across the world. The results of Crime Lab and Education Lab projects have been published in leading peer-reviewed scientific outlets like Science, the Quarterly Journal of Economics, and the Journal of Policy Analysis and Management, and featured in leading national news outlets like the New York Times, Washington Post, Wall Street Journal, National Public Radio, and PBS News Hour. About our Machine Learning Portfolio The success of machine learning in the commercial sector raises the promising idea that the combination of machine learning and large administrative datasets can lead to similar benefits in the public sector. At the same time, machine learning is fundamentally different than the causal inference questions that have been the mainstay of empirical policy research, resulting in hesitation and uncertainty about how and when to use these new techniques. This uncertainty is compounded by concerns that the naïve use of historical data will result in tools that will perpetuate biases and aggravate problems, rather than solve them. The goal of both the Crime Lab and the Education Lab is to identify problems where machine learning can provide a significant social benefit1, and to solve the resulting conceptual challenges associated with the creation, evaluation, and deployment of predictive models. We believe that proper and judicious application of machine learning can yield the benefits of these techniques while mitigating harms, but that doing so will require careful thinking and new research at the intersection of computer science and the social sciences. We are particularly interested in novel approaches to prediction and evaluation in the presence of biased and censored data2, and in approaches to optimally combining the prediction from a machine learning algorithm with the expertise and private information of a human decision-maker. Crime Lab offers a unique opportunity to work on these conceptual challenges in the context of problems faced by policy makers every day. We work with our partners to develop a project all the way from the basic research question to implementation and ultimate evaluation. Through this work, we hope to contribute to the development of best practices on how to effectively and responsibly implement predictive tools in public policy.

Job Summary
 

The job manages multiple related teams of managers and professional staff responsible for a research project or contributes to the scientific direction of a research resource.

Responsibilities

• Provides scientific direction on all projects, ensures the rigor and quality of all work.
• Manages and mentors research and project staff.
• Helps set and contribute to organizational priorities as part of the management team.
• Reporting to the Managing Director, works collaboratively with senior faculty members at the University of Chicago and elsewhere, other research directors, and with partner agencies.
• Contributes to the scientific content of research proposal.
• Assists the Executive Director, Managing Director, and other faculty affiliates in supporting fundraising, dissemination, and policymaker outreach activities.
• Contributes to strategic conversation about which new projects and initiatives the Crime Lab and Education Lab should take on.
• Manages staff and demonstrates leadership on organizational initiatives .
• Manages, plans, and evaluates regulatory elements of multiple or complex research projects for a designated department, program, or central unit
• Establishes standard operating procedures for continuous review and development of quality improvement plans for our unit. Advances research objectives through participation in professional committees and presentations. Ensures timely reporting of research data, following study specific guidelines.
• Performs other related work as needed.

Minimum Qualifications
 

Education:

Minimum requirements include a PhD in related field.

---
Work Experience:

Minimum requirements include knowledge and skills developed through 7+ years of work experience in a related job discipline.

---
Certifications:

---

Preferred Qualifications

Education:

• Ph.D. in computer science, statistics, economics, public policy, or other related quantitative discipline, and ideally will have defended their doctoral dissertation by Fall 2022. 

Experience:

• Previous experience in program evaluation and econometrics is a plus. 
• Preference will be given to those who understand and use methods of causal inference, who have experience evaluating the performance of predictive models in real-world settings, and who have proven experience working with interdisciplinary research teams and agency and community stakeholders. 
• Experience with machine learning applications in urban or social science.

Technical Skills or Knowledge:

• Expertise in machine learning or data science research, and an interest in finding and testing innovative solutions to urban problems.
• Advanced knowledge of machine learning and statistics.
• Experience developing reproducible and maintainable code.
• Knowledge of causal inference methods preferred.
• Communicate insights from data to technical and non-technical audiences.
• Knowledge of regulatory policies and procedures.

Preferred Competencies

• Strategic leadership skills.
• Supervisory skills.
• Analytical skills.
• Problem-solving skills.
• Attention to detail.
• Organizational skills.
• Verbal and written communication skills.
• Work independently and as part of a team.

Application Documents

• Resume/CV (required)
• Cover Letter (required)
• References Contact Information (3)(required)
• Writing Sample (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
 

Research

Role Impact
 

People Manager

FLSA Status
 

Exempt

Pay Frequency
 

Monthly

Scheduled Weekly Hours
 

37.5

Benefits Eligible
 

Yes

COVID-19 Vaccination or Approved Medical or Religious Exemption Required
 

Yes

Drug Test Required
 

No

Health Screen Required
 

No

Motor Vehicle Record Inquiry Required
 

No

Posting Statement
 

Effective October 15, 2021, the University of Chicago requires COVID vaccination for all students, faculty, and staff members. Individuals may claim exemption from the vaccine requirement for medical or religious reasons.
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.

 

The University of Chicago's Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu . Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.

Department
 

UL Urban Crime Lab

About the Department
 

The Crime Lab and its sister organization, the Education Lab, is a non-partisan faculty-led research center at the University of Chicago directed by professors Jens Ludwig and Jonathan Guryan. Our mission is to help design, test, and scale solutions to some of our nation’s most pressing and persistent social problems. We use the power of research and data science to help public sector agencies improve social conditions for the people they serve. Our focus on partnering with the public sector is motivated by the recognition that only the government has the scale of operations to translate promising solutions into large-scale social change. Our work seeks to have impact in the world through two key pathways. The first pathway to impact is directly through the design and testing of promising policy levers in conjunction with our government and NGO partners. Past projects have, for example, helped the city of Chicago re-orient much of its violence strategy towards greater use of social programs, informed the Chicago Public Schools decision to allocate millions of dollars to intensive high-dosage tutoring for children growing up in some of the city’s most economically disadvantaged communities, supported the implementation of reforms at the Chicago Police Department initiated by the Obama Administration’s Department of Justice civil rights division, and worked with the New York City’s mayor’s office on the effort to close the long-troubled Rikers Island jail without compromising public safety in the city. The second pathway to impact is by drawing generalizable lessons from these local R&D initiatives that are intended to inform scientific and policy efforts across the country, or even across the world. The results of Crime Lab and Education Lab projects have been published in leading peer-reviewed scientific outlets like Science, the Quarterly Journal of Economics, and the Journal of Policy Analysis and Management, and featured in leading national news outlets like the New York Times, Washington Post, Wall Street Journal, National Public Radio, and PBS News Hour. About our Machine Learning Portfolio The success of machine learning in the commercial sector raises the promising idea that the combination of machine learning and large administrative datasets can lead to similar benefits in the public sector. At the same time, machine learning is fundamentally different than the causal inference questions that have been the mainstay of empirical policy research, resulting in hesitation and uncertainty about how and when to use these new techniques. This uncertainty is compounded by concerns that the naïve use of historical data will result in tools that will perpetuate biases and aggravate problems, rather than solve them. The goal of both the Crime Lab and the Education Lab is to identify problems where machine learning can provide a significant social benefit1, and to solve the resulting conceptual challenges associated with the creation, evaluation, and deployment of predictive models. We believe that proper and judicious application of machine learning can yield the benefits of these techniques while mitigating harms, but that doing so will require careful thinking and new research at the intersection of computer science and the social sciences. We are particularly interested in novel approaches to prediction and evaluation in the presence of biased and censored data2, and in approaches to optimally combining the prediction from a machine learning algorithm with the expertise and private information of a human decision-maker. Crime Lab offers a unique opportunity to work on these conceptual challenges in the context of problems faced by policy makers every day. We work with our partners to develop a project all the way from the basic research question to implementation and ultimate evaluation. Through this work, we hope to contribute to the development of best practices on how to effectively and responsibly implement predictive tools in public policy.

Job Summary
 

The job manages multiple related teams of managers and professional staff responsible for a research project or contributes to the scientific direction of a research resource.

Responsibilities

• Provides scientific direction on all projects, ensures the rigor and quality of all work.
• Manages and mentors research and project staff.
• Helps set and contribute to organizational priorities as part of the management team.
• Reporting to the Managing Director, works collaboratively with senior faculty members at the University of Chicago and elsewhere, other research directors, and with partner agencies.
• Contributes to the scientific content of research proposal.
• Assists the Executive Director, Managing Director, and other faculty affiliates in supporting fundraising, dissemination, and policymaker outreach activities.
• Contributes to strategic conversation about which new projects and initiatives the Crime Lab and Education Lab should take on.
• Manages staff and demonstrates leadership on organizational initiatives .
• Manages, plans, and evaluates regulatory elements of multiple or complex research projects for a designated department, program, or central unit
• Establishes standard operating procedures for continuous review and development of quality improvement plans for our unit. Advances research objectives through participation in professional committees and presentations. Ensures timely reporting of research data, following study specific guidelines.
• Performs other related work as needed.

Minimum Qualifications
 

Education:

Minimum requirements include a PhD in related field.

---
Work Experience:

Minimum requirements include knowledge and skills developed through 7+ years of work experience in a related job discipline.

---
Certifications:

---

Preferred Qualifications

Education:

• Ph.D. in computer science, statistics, economics, public policy, or other related quantitative discipline, and ideally will have defended their doctoral dissertation by Fall 2022. 

Experience:

• Previous experience in program evaluation and econometrics is a plus. 
• Preference will be given to those who understand and use methods of causal inference, who have experience evaluating the performance of predictive models in real-world settings, and who have proven experience working with interdisciplinary research teams and agency and community stakeholders. 
• Experience with machine learning applications in urban or social science.

Technical Skills or Knowledge:

• Expertise in machine learning or data science research, and an interest in finding and testing innovative solutions to urban problems.
• Advanced knowledge of machine learning and statistics.
• Experience developing reproducible and maintainable code.
• Knowledge of causal inference methods preferred.
• Communicate insights from data to technical and non-technical audiences.
• Knowledge of regulatory policies and procedures.

Preferred Competencies

• Strategic leadership skills.
• Supervisory skills.
• Analytical skills.
• Problem-solving skills.
• Attention to detail.
• Organizational skills.
• Verbal and written communication skills.
• Work independently and as part of a team.

Application Documents

• Resume/CV (required)
• Cover Letter (required)
• References Contact Information (3)(required)
• Writing Sample (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
 

Research

Role Impact
 

People Manager

FLSA Status
 

Exempt

Pay Frequency
 

Monthly

Scheduled Weekly Hours
 

37.5

Benefits Eligible
 

Yes

COVID-19 Vaccination or Approved Medical or Religious Exemption Required
 

Yes

Drug Test Required
 

No

Health Screen Required
 

No

Motor Vehicle Record Inquiry Required
 

No

Posting Statement
 

Effective October 15, 2021, the University of Chicago requires COVID vaccination for all students, faculty, and staff members. Individuals may claim exemption from the vaccine requirement for medical or religious reasons.
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.

 

The University of Chicago's Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu . Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.