Full-Stack Machine Learning Engineer​/Data Scientist

Company Description

By working at Harvard University, you join a vibrant community that advances Harvard’s world‑changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.

The Faculty of Arts and Sciences (FAS) is the historic heart of Harvard University. It is the home of Harvard’s undergraduate program (Harvard College, founded in 1636) as well as all of Harvard’s Ph.D. programs (the Harvard Kenneth

C. Griffin Graduate School of Arts and Sciences, founded in 1872), Harvard Athletics and the Division of Continuing Education. The 40 academic departments and 30+ centers of the FAS support a community unparalleled in its academic excellence across the broadest range of liberal arts and sciences disciplines. Together, the FAS seeks to foster an environment of ambition, curiosity and shared commitment to knowledge and truth that elicits excellence from all members of our community and prepares the next generation of leaders through a transformative educational experience.

Participate in the design of software that supports and enriches research productivity and reliability; implement software solutions. Develop software and data services with researchers to ensure that modern standards of reproducible code are kept.

Lead analytic development across several ongoing clinical research initiatives and enrich research productivity and reliability; implement software solutions. Ensure that modern standards of reproducible code are kept.

A research lab studying suicide in the Department of Psychology at Harvard University is seeking to hire a Full‑Stack Machine Learning Engineer (MLE) / Data Scientist (DS) to support the end‑to‑end management, analysis, and visualization of behavioral and clinical data streams. The full‑stack MLE/DS will work on studies aimed at advancing the understanding, prediction, and treatment of suicidal thoughts and behaviors.

The position involves working on scalable data pipelines, integrating multimodal data (e.g., data from smartphone‑based surveys, passive smartphone/wearable monitors, social media platforms, electronic health records), and helping to deploy analytic tools that can generate actionable insights (e.g., visualizations, algorithms) in real‑time.

• Work with the research team to support the design, development, and implementation of ML models.
• Support infrastructure for cleaning, processing, analyzing, and visualization of various data types (e.g., GPS data scraped from smartphones, accelerometer data from wearable devices, digital phenotyping data, etc.).
• Support experiments to evaluate model performance, perform error analysis, and suggest and implement improvements.
• Conduct higher‑level analysis of data and supervise analyses performed by other members of the lab.
• Integrate data across workflows (e.g., digital phenotyping, behavioral, and clinical data).
• Help to develop and support a secure, scalable dashboard or lightweight clinical app that synthesizes data and provides visualizations in real‑time.
• Deploy modular, reusable visualization components and maintain version‑controlled code repositories.
• Work closely with university and Harvard teaching hospital‑based IT teams to ensure interoperability, reliability, and clinical relevance.
• Assist with preparation of grant applications, presentations, and publications.

Occasionally required to work outside of normal business hours, and may be contacted during off hours.

• Minimum of five years’ post‑secondary education or relevant work experience.

• 3‑5+ years of hands‑on experience with time‑series data, sensor data, or biomedical/wearable data.
• Proficiency in one or more programming languages (Python and/or JavaScript preferred), including libraries for ML (Tensor Flow, PyTorch), data engineering (pandas, Num Py), and visualization (Plotly, Dash, Bokeh).
• Experience deploying dashboards or apps (e.g., Dash,…