HIRE FROM US
Access more than 4000 top candidates seeking new roles in Data Science, Machine Learning, Artificial Intelligence, Quant Research/Finance, Software Engineering, Quantum Computing, UX Research, Professional Writing, and more!
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Our Recruitment Solutions
Whether you are looking to save time and money through our Project-Based Recruitment solution or Traditional Staffing Agency model, our programs and services are designed to help you find ideal candidates faster and more efficiently.
Project-Based Recruitment
For just $5,000, your organization can submit one dataset or challenge per cohort to the Erdős Institute’s Project-Based Recruitment Program. Each project is tackled by teams of talented Ph.D. students and postdoctoral researchers participating in our Data Science, UX Research, or Deep Learning Boot Camps. These individuals are eager to showcase their skills and solve real-world problems that matter to you.
How it works
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Submit a Project
Provide a dataset or a business challenge for our students to solve. This can include tasks such as data cleaning, visualization, predictive modeling, or optimization challenges tailored to your needs. -
Engage with Talent
Watch as our participants—eager to demonstrate their skills—deliver innovative solutions to your problem. This hands-on experience allows you to see their talent and creativity in action, with no commitment required beyond the project. -
Evaluate and Recruit
Gain access to a pool of motivated, highly trained Ph.D. candidates who are actively seeking to transition into data-driven industry roles. Use the insights from the project to identify top performers and potentially recruit them into your organization.
Traditional Staffing Solution
For just 20% of the first-year base salary, the Erdős Institute’s Traditional Staffing Solution takes the hassle out of hiring. We do the work for you, conducting an on-demand, targeted search across our extensive talent pool of Ph.D. graduates, postdocs, and alumni from our Data Science, UX Research, and Deep Learning Boot Camps.
Our team identifies and pre-screens candidates tailored to your specific needs, ensuring you receive only the most qualified and motivated talent. Whether you're hiring for a technical, analytical, or research-based role, we connect you with individuals who are not only experts in their fields, but also eager to make an impact in your organization.
Examples of projects from prior cohorts
SUMMER 2025
TEAM
Data Science Boot Camp
Tuning Up Music Highway
James O'Quinn, Yang Mo, john hurtado cadavid, Ruixuan Ding, Chilambwe Natasha Wapamenshi
Known as the most dangerous highway in Tennessee, Music Highway, the stretch of Interstate 40 between Memphis and Nashville, could use a serious tuning up. This project investigates the effectiveness and cost-efficiency of potential physical safety interventions along its Madison and Henderson County segments, with the goal of reducing crash severity. We used a data-driven geospatial modeling approach to assess whether adding specific safety features to targeted segments predicts statistically significant changes in crash injury outcomes.



SPRING 2025
TEAM
Data Science Boot Camp
Who Regulates the Regulators?
Jared Able, Joshua Jackson, Zachary Brennan, Alexandria Wheeler, Nicholas Geiser
With recent major cuts to governmental regulation agencies in the US, we investigate whether those cuts are justified. In particular, we analyze the efficacy of RGGI, a state-level cap-and-trade program designed to regulate CO2 emissions in power plants. By using synthetic controls, we answer the counterfactual question: "how would CO2 emissions look in a world where RGGI was never enacted?".



FALL 2024
TEAM
Data Science Boot Camp
Predicting Problematic Internet Use
Daniel Visscher, Emilie Wiesner, Aaron Weinberg
Internet use has been identified by researchers as having the potential to rise to the level of addiction, with associated increased rates of anxiety and depression. Identifying cases of problematic internet usage currently requires evaluation by an expert, however, which is a significant impediment to screening children and adolescents across society. One potential solution is to rely on data that is more easily and uniformly collected: the kind collected by a family physician, a simple survey, or by a smartwatch. The research question this project sets out to answer is: “Can we predict the level of problematic internet usage exhibited by children and adolescents, based on their physical activity and survey responses?”


