Full-Stack Machine Learning Engineer / Data Scientist

Job Summary:

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.

Job-Specific Responsibilities:

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.  

The MLE will join a dynamic, multi-site team working at the intersection of machine learning, digital phenotyping, pediatric mental health, and real-time clinical decision support on projects aimed at improving identification of, and intervention on, mental health problems (e.g., suicide) using rich data sources. The successful applicant will have strong programming skills and technical expertise in ML to execute tasks independently, advanced data management, analysis, and visualization skills. This role is ideal for someone who wants to work on mental health research with real-world implications. Responsibilities include:

• 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. 

Working Conditions:

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