Job Description

Major Responsibilities:

Collaborate with stakeholders and subject matter experts to define and refine data-driven initiatives that enhance healthcare operations and outcomes. Develop a strong understanding of complex, high-volume, and heterogeneous data, ensuring rigorous data development, cleaning, and validation. Apply machine learning and statistical modeling techniques, including regression, classification, clustering, time series forecasting, and ensemble methods. Utilize LLM-based text analytics and NLP techniques to extract insights from unstructured data sources such as clinical notes and reports. Perform exploratory data analysis and build structured analytical workflows using Python, R, and SQL. Communicate findings and methodologies effectively to both technical and non-technical audiences through reports, presentations, and interactive visualizations. Independently manage projects, setting clear objectives, benchmarks, and milestones while balancing iterative development with accountability. Develop well-documented, reusable code on cloud computing platforms to support analytical pipelines. Follow best practices in collaborative software development, including version control (Git) and modular code design.

Licensure, Registration, and/or Certification Required:

None

Education Required:

PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Biomedical Informatics or a related field, OR a Master's degree with 2-4 years of applied experience in data science or machine learning.

Experience Required:

Typically requires 2-4 years of experience in solving complex problems in an analytical environment

Knowledge, Skills & Abilities Required:

Extensive experience working with large-scale structured and unstructured data, ensuring data quality and interpretability. Proficiency in Python and/or R for advanced data analysis, machine learning, and model deployment, along with strong SQL skills for working with large datasets. Expertise in statistical modeling, machine learning, and AI techniques, including regression, classification, time series forecasting, and ensemble methods, with experience in at least some advanced techniques (e.g., Bayesian inference, causal modeling, deep learning, LLMs, NLP, or knowledge engineering). Demonstrated ability to lead collaborations with stakeholders, define analytics initiatives, and mentor junior team members. Strong critical thinking and problem-solving skills, with an iterative and pragmatic approach to real-world challenges. Proven ability to translate complex data into actionable insights, using storytelling, interactive dashboards, and presentations. Ability to independently manage multiple complex projects, set clear objectives, and drive high-impact initiatives. Experience writing clean, reusable, and well-documented code and working with containerization tools like Docker for model deployment.

Physical Requirements and Working Conditions:

Requires extensive amounts of time working at a computer. Work can be performed in an office environment or remotely.

Preferred Job Requirements

Experience working with healthcare data (EHR, claims, clinical notes, imaging, operational workflows). Exposure to MLOps principles, including model monitoring, lifecycle management, and CI/CD for ML models. Experience integrating AI/ML models into production environments, such as API-based decision support or real-time analytics.
This job description indicates the general nature and level of work expected of the incumbent. It is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities required of the incumbent. Incumbent may be required to perform other related duties.