"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

Basic Qualifications

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

Additional Qualifications and Skills

• 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 (TensorFlow, PyTorch), data engineering (pandas, NumPy), and visualization (Plotly, Dash, Bokeh).
• Experience deploying dashboards or apps (e.g., Dash, Streamlit, React, Flask, or similar).
• Experience with real-time or streaming data pipelines.
• Expert-level knowledge of statistical programming, particularly R (tidyverse, ggplot2) and R Markdown.
• Strong understanding of ML approaches for classification, anomaly detection, and prediction using high-frequency data.
• Experience with multilevel longitudinal data, missing data strategies, and clinical outcome modeling.
• Experience with EHR data, REDCap, Qualtrics, or hospital-based informatics systems.

Certificates and Licenses

• Completion of Harvard IT Academy specified foundational courses (or external equivalent) preferred

Additional Information

• Standard Hours/Schedule: 35 hours per week
• Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position
• Pre-Employment Screening: Identity, Criminal
• This is a one-year term position with renewal dependent upon continuation of funding.
• All formal offers will be made by FAS Human Resources.

Work Format Details

This position has been determined by school or unit leaders that some of the duties and responsibilities can be effectively performed at a non-Harvard location. The work schedule and location will be set by the department at its discretion and based upon operational needs. When not working at a Harvard or Harvard-designated location, employees in hybrid positions must work in a Harvard registered state in compliance with the University’s Policy on Employment Outside of Massachusetts. Additional details will be discussed during the interview process. Certain visa types and funding sources may limit work location. Individuals must meet work location sponsorship requirements prior to employment.

Salary Grade and Ranges

This position is salary grade level 057. Please visit Harvard's Salary Ranges to view the corresponding salary range and related information.

Benefits

Harvard offers a comprehensive benefits package that is designed to support a healthy work-life balance and your physical, mental and financial wellbeing. Because here, you are what matters. Our benefits include, but are not limited to:

• Generous paid time off including parental leave
• Medical, dental, and vision health insurance coverage starting on day one
• Retirement plans with university contributions
• Wellbeing and mental health resources
• Support for families and caregivers
• Professional development opportunities including tuition assistance and reimbursement
• Commuter benefits, discounts and campus perks

Learn more about these and additional benefits on our Benefits & Wellbeing Page.