There are so many interesting and important things to do in statistical modeling, causal inference, and social science, and so many places for recent graduates to jump in. Here are two opportunities that happen to have come in the mail on the same day.

Angela Aidala sends along this ad for a research analyst focused on data visualization and design at the Brennan Center:

The Brennan Center for Justice at NYU School of Law is a nonpartisan law and policy institute that seeks to improve our systems of democracy and justice. We work to hold our political institutions and laws accountable to the twin American ideals of democracy and equal justice for all. Among our core priorities, we fight to protect voting rights, end mass incarceration, strengthen checks and balances, and preserve Constitutional protection in the fight against terrorism. Part think-tank, part advocacy group, part cutting-edge communications hub, we start with rigorous research. We craft innovative policies. And we fight for them — in Congress and the states, the courts, and in the court of public opinion.

The Brennan Center is seeking a talented Research Analyst (Data Visualization Design) to conduct research with Brennan Center program project teams and as part of the Research Department.

Position Overview:

The Research Analyst will work with colleagues across Brennan Center, and with external partners, on a wide range of projects. The Research Analyst will have a particular responsibility for partnering with researchers and communications experts in creating data visualizations to effectively communicate research on democracy and justice.

Responsibilities:

• Design and produce effective data visualizations.

• Collaborate with researchers and staff across all Brennan Center programs and departments to better leverage data visualization in the pursuit of our mission.

• Collaborate with communications professionals and external partners on data visualizations related to democracy and justice.

• Apply knowledge of research methods and data visualization practices to inform the design of research, collection and cleaning of data, data management, and statistical analysis.

• Conduct empirical research and data analysis for Brennan Center policy and legal advocacy efforts and public communication efforts.

• Draft and edit writeups of research output, including reports, academic journal articles, and other public communications as needed.

• Critically evaluate research and communicating the strengths and weaknesses of research to diverse audiences.

• Develop and maintain a high level of practical expertise on best practices for data visualization and research methodology.

• Develop and maintain a high level of knowledge of substantive research on democracy and justice.

• Provide mentorship and training for staff in areas of expertise as appropriate.

Qualifications:

The ideal candidate will offer some combination of the following experiences and qualifications. We recognize that many excellent candidates may not have all these experiences and qualifications and we will provide support for the successful candidate to acquire additional skills. We hope that you will apply as long as you believe you could contribute to our organization and our work.

• A master’s degree and at least two years of research experience in data visualization.

• Demonstrated expertise in and portfolio of data visualization projects.

• Experience with the development of data visualization at multiple stages of the research process and as part of a broader communication strategy reaching multiple audiences, including the public, policymakers, advocates, and for academic or technical audiences.

• Experience developing data visualizations appropriately tailored for public communication strategies on varied media, including but not limited to print reports, op eds, websites, social media, and interactives.

• Experience working on data visualization of geographic data, data visualizations that effectively visualize statistical uncertainty, and the use of appropriate data visualization for advanced statistical research.

• Familiarity with commonly used data visualization tools and software such as Datawrapper, D3, Flourish, and Tableau, or others.

• Strong fluency with data science techniques, and advanced experience using R, Stata, or another comparable data analysis programs. Substantial experience in dataset management.

• Exceptional attention to detail and accuracy in all work, with proven ability to plan for rigorous arms-length research review and other techniques for bulletproofing findings.

• Understanding of how to engage in research through a racial equity approach and experience working with data visualizations that effectively portray the diverse experiences of Americans in our systems of democracy and justice.

• Demonstrated ability to collaborate with diverse stakeholders in designing and implementing research and to communicate technical research findings to broad audiences.

• Excellent interpersonal skills, including proven ability to work effectively within teams, and to work collaboratively with editors, other staff, and outside partners.

• Commitment to the mission and values of the Brennan Center.

They’re asking for a lot—it’s hard to find someone who can write and also do visualization and research. Seems like it could be a great job for the right person.

Unrelatedly, Nicolas Bousquet informs us:

I am writing to inform us of “a post-doctoral position in Bayesian modeling at Ecole Polytechnique in Palaiseau (France), in collaboration with Électricité de France. We hope that this subject will be of interest to some young PhDs or future PhDs who are familiar with the mathematical aspects of Bayesian modeling, and who are interested in exploring approximations of prior distributions.

From the ad:

The Sustainable Energies Chair of the Ecole Polytechnique is offering a 1-year post-doc position, renewable once and starting before December 2024, on a subject related to improving the objectivity of Bayesian modeling rules in view of supporting interpretable decision. . . . Bayesian modeling choices play an important role in the context of forecasting and decision support through statistical learning applied to costly or rare data. These are characteristic of the risk situations affecting the industrial world. Their promise is to be able to model the uncertain knowledge required to inform models in addition to available data, or even in their absence in the event of a regime change. Major applications related to decarbonated energies are the following (among others):

• rapid location of nuclear material in waste packages, in order to apply focused spectrometry techniques to identify fission products;
• the reliability of important or even critical industrial components, such as steam generators, batteries, valves, etc. ;
• quantification of the intensity of extreme natural phenomena (torrential rain, marine submersion, floods, etc.);
• calibration of technico-economic models used to optimize the design and operation of energy parks, particularly when the depth of history is shallow (e.g. offshore park deployments).

The work will aim to bring together a set of known methodological constraints, still separated, into a single approach that will extend methodologies already established for sub-families of models (in particular exponential families and conjugate models). Such constraints are, for instance, related to prior-data conflict or q-vague convergence. . . . Such approaches are interpretable because they consider that the available information can be assimilated to that provided by data not directly known, but that it can be manipulated by explicit approximation techniques outside conjugate families. . . .

The work will be supervised by Professor Josselin Garnier (École Polytechnique / CMAP-Centre de Mathématiques Appliquées), in collaboration with Dr. Nicolas Bousquet, senior researcher at EDF R&D. . . .

The candidate should have a PhD thesis in statistics or applied mathematics, with a good knowledge of Bayesian statistics. A good knowledge of mathematical tools related to the approximation of probability distributions and non-convex optimization would be a plus. . . . CMAP conducts theoret- ical and numerical research on mathematics in interaction with other sciences (biology, economics, computer science, mechanics, physics, etc) or in connection with industrial or societal applications. Its specialties are numerical analysis, scientific computing, control, artificial intelligence, modeling, optimization, probability, signals, statistics, etc.

They didn’t specifically mention Stan, but I’m sure it would be useful!