Job Description
This position involves designing, training, and deploying predictive models that support key decision-making across clinical, research, and operational teams. You’ll work with structured and unstructured data, evaluate different modeling approaches, and build pipelines that can run consistently in production. The work isn’t limited to modeling—you’ll also help refine data workflows, validate results with subject matter experts, and guide teams on how to integrate AI outputs into their processes. Because this role supports life science initiatives, the ability to interpret patterns responsibly and explain results clearly is important.
Responsibilities
• Build and refine predictive models using machine learning techniques suited for clinical and operational datasets.
• Develop data pipelines and feature engineering logic that support model training and long-term scalability.
• Evaluate model performance, tune hyperparameters, and compare different algorithms based on real-world impact.
• Work with domain experts to validate results and ensure predictions align with clinical or research expectations.
• Deploy models into production environments and manage ongoing monitoring, updates, and versioning.
• Collaborate with data engineering teams to improve data availability, quality, and consistency.
• Review business requirements and translate them into modeling strategies and structured workflows.
• Prepare documentation covering model logic, assumptions, validation methods, and operational guidelines.
• Support teams in adopting AI-driven insights and help ensure outputs are used responsibly.
Required Skills
• Strong experience building ML models using Python, TensorFlow, PyTorch, Scikit-learn, and related libraries.
• Solid background in predictive analytics, time series modeling, classification, and regression techniques.
• Hands-on experience designing data pipelines using tools like Airflow, Spark, or similar frameworks.
• Familiarity with cloud environments such as AWS, Azure, or GCP for ML training and deployment.
• Understanding of life science or healthcare data, including typical patterns, limitations, and validation needs.
• Ability to explain model behavior and communicate complex ideas to non-technical stakeholders.
• Strong debugging skills and the ability to troubleshoot issues in both modeling and production environments.
Equal Opportunity Employer
We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.