Research Data Analyst

About the role

We are seeking a Research Data Analyst to join our team. This is an analysis-first role embedded with our data science and research personnel: you will design, run, and interpret analyses of experimental data across the full lifecycle of a research study.

Responsibilities

• Plan and execute analyses of experimental data with support from our research and data science team: co-develop a clear hypothesis, specify expected outcomes, and select appropriate statistical methods before running the analysis.
• Use Python and AI-assisted coding tools to build analysis pipelines and statistical tests, critically reviewing generated code for correctness.
• Validate results end to end: check data quality, verify assumptions, spot anomalies and subtle errors, and trace unexpected results to their root cause.
• Interpret findings in the context of the experimental design, and be clear about what the results do and do not support.
• Present results of analyses to team members on an ongoing basis.
• Write clear, well-organized analysis reports documenting methods, results, and interpretation.
• Prepare, maintain, and update technical documentation.
• Attend weekly project meetings and collaborate closely with data science and research team members through study design, execution, analysis, and interpretation.

Requirements

• Bachelor's degree in Neuroscience, Psychology, Computer Science, Statistics, Data Science, or a related quantitative or bioscience field.
• Working proficiency in Python and scientific computing libraries (e.g., NumPy, Pandas, Matplotlib) for data analysis and statistical work.
• A solid understanding of the research workflow: experimental design, hypothesis formulation, execution, analysis, and interpretation of results.
• The ability to critically evaluate analysis code and outputs — catching small errors matters, because the effects we measure are small.
• Strong attention to detail and a root-cause mindset: you don't stop at "this looks wrong" — you dig in and find out why.
• Clear written communication and the ability to produce readable reports of methods and findings.
• Motivation to learn and improve. You don't need to be an expert in any one field, but you should be eager to build on your skills.
• An outside-the-box mindset: we don't need a robot — we want someone willing to take risks, try new approaches, and push an analysis beyond what an AI prompt hands back, because you care to understand what's really going on.

Nice to Have

• Master's degree in Computer Science, Statistics, Data Science, Neuroscience, Psychology, or a related field.
• Laboratory or human subjects research experience (academic lab, thesis project, internship, or publication).
• Experience with research statistics (e.g., parametric, nonparametric, or Bayesian statistics).
• Experience using AI coding assistants effectively — prompting, reviewing, and correcting generated code.
• Experience preparing manuscripts for publication.

Benefits

• Competitive salary and stock option plan.
• Flexible paid time off (vacation, sick leave, and public holidays).
• Flexible schedules.
• Company health care plan: Medical, dental, and vision insurance.
• Short and long term disability insurance.
• Life insurance policy.
• 401(k).
• Commuter benefits for parking, public transit, carshares, etc.
• Mothers' room.
• Fully stocked kitchen.
• Opportunities for continuing education.

Pay

$65,000-$80,000 annually.