Full-Stack Machine Learning Engineer / Data Scientist

About This Role

This role involves leading analytic development for clinical research, implementing software solutions, and ensuring reproducible code standards. It focuses on the end-to-end management, analysis, and visualization of behavioral and clinical data streams, particularly for understanding and treating suicidal thoughts and behaviors.

Responsibilities

• 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.
• 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 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.
• Collaborate with researchers in the design, planning, and implementation of analyses that enrich research productivity and reliability.
• Build and maintain aspects of software code and custom data processing pipelines for complex environments.
• Assist with preparation of grant applications, presentations, and publications.

Requirements

• Minimum of three 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 (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

Qualifications

• 3-5+ years of hands-on experience with time-series data, sensor data, or biomedical/wearable data.

Nice to Have

• Experience with multilevel longitudinal data, missing data strategies, and clinical outcome modeling
• Experience with EHR data, REDCap, Qualtrics, or hospital-based informatics systems

Skills

* Required skills