About Us:
The Michael J. Fox Foundation for Parkinson’s Research (MJFF) was founded in 2000 with a single defined objective: accelerating meaningful therapeutic advances and, ultimately, a cure for Parkinson’s disease (PD). The Foundation provides a distinctive combination of scientific, business, and media assets to this core focus. This triple-pronged, singularly-defined strategy towards disease research enables a dynamic, motivated, and young organization. MJFF has quickly grown into the largest global funder of Parkinson’s disease research outside of the federal government and has funded over $800 million in research projects across academic and industry labs worldwide.
MJFF raises over $90 million annually. We deliberately have no endowment or reserve because we believe that our capital needs to be invested today to find a cure for PD as quickly as possible. We start our fundraising from zero each year and deploy funds raised as quickly as possible, in a strategic, targeted, and vetted manner. The Foundation has approximately 150 full-time employees who are based in New York City. All interns are paid a $15 stipend per day.
Position description:
As a Data Science Intern with the Research Partnerships team, you will use data collected from MJFF-sponsored research studies to develop and communicate insights that can shape study operations. You will have the opportunity to work with our population-based cohort studies and apply novel quantitative methods to develop models that explain problems of interest. In particular, you will focus on modeling study data to understand/explain how participants interact with the study and contribute data.
Over the course of the summer, you will work alongside our data team to craft a project that extracts meaningful insights from our cohort study data and make recommendations to influence important study objectives such as retention and compliance. Retention and compliance are indicators used in cohort-studies to measure what proportion of the population stays in the study, and what proportion of the population does what is asked of them. Application of concepts from marketing analytics such as a Bayesian customer lifetime value modeling adapted to non-monetary research participation setting is one opportunity of interest that can explain study retention and compliance. Deriving insights based on such modeling can help the study leadership better understand participant behavior, and design interventions to that end.
The ideal person for this role is a highly analytical graduate-level student in a quantitative field who is a creative problem-solver, is eager for an impactful opportunity to work with real-world data and contribute to PD research through open-access work.
This role will be responsible for:
- Applying data analysis and statistical modeling skills to analyze underlying patterns of study participation
- Developing recommendations on approaches to increase study participation, and communicate results to multiple research and product team stakeholders.
- Sharing the data analysis and insights with the research community through open-access platforms like GitHub.
- Collaborating along-side our data analysts, and research teams to gain a broad understanding of the study cohorts to inform your analyses
Desired skills and experience:
- Currently pursuing graduate degree in a quantitative field (e.g., mathematics, statistics, epidemiology, economics, operations research, quantitative social science, etc.).
- Ability to break down complex problems, define a solution, and implement advanced quantitative methods
- Strong experience in R or Python necessary. Familiarity with SQL helpful.
- Technical understanding of Bayesian methods, probability theory, predictive modeling, and statistical analysis (classification, regression, etc).
- Solid oral and written communication skills, especially around sharing analytical concepts to non-analytical audiences.
- Intellectual curiosity and a desire to contribute to the open-access community.
- Availability to work at least 20 hours per week.
How to Apply
Interested candidates should submit a resume and cover letter describing your specific qualifications and interest to the following link. Applicants who best match the position description will be contacted. Submissions without cover letters will not be considered.
The Michael J. Fox Foundation is an equal opportunity employer.