RCI-SNFI-10310 Principal Data Scientist/ Disease onset or prognostic modeling

Work Schedule: M-F 9-5 EST

100% Remote

Title: Principal Data Scientist

Possibility of extension

Must Have :

Disease onset or prognostic modeling.

• Min of 8 years of related experience
• Pharma and health care experience
• ML experience required
• Federated analytics and federatML – must have
• Deep Learning transformer architecture, transformer-based architecture is required
• Application in healthcare is essential, EMR, claims data, genomic data exp required
• Python, R
• Experience in healthcare/ biostats/Case control studies etc.

Nice to Have :

• C++ (highly desirable)

We are seeking an experienced and visionary Principal Data Scientist to lead our efforts in developing advanced predictive models and AI solutions for healthcare.

• The ideal candidate will possess a deep understanding of machine learning methodologies, a proven track record of delivering impactful data-driven solutions in a real-world setting, and the ability to drive innovation across diverse therapeutic areas.

Main responsibilities. The overall purpose and main responsibilities are listed below:

• Lead the design, development, and deployment of cutting-edge predictive models using various machine learning and AI techniques, including tree-based models (e.g., XGBoost) and transformer-based architectures (e.g., BERT), for early disease detection and proactive interventions.
• Drive the strategic direction of data science initiatives across multiple therapy areas, identifying opportunities to leverage real-world data (e.g., open claims data, EHR) for improved patient outcomes and drug development, including the use of federated analytics and federatML.
• Provide technical leadership and mentorship to a team of data scientists, fostering a culture of innovation, rigorous experimentation, and best practices in MLOps.
• Evaluate and select appropriate modeling techniques and performance metrics (e.g., Precision, Recall, Bayes factor, NNT) based on specific problem statements and business objectives.
• Collaborate closely with cross-functional teams including business owners, payers, clinicians, epidemiologists, statisticians, and IT to translate complex business problems into tractable data science solutions for deployment in real world.
• Stay abreast of the latest advancements in machine learning, deep learning, and AI, and proactively integrate novel approaches into our predictive modeling capabilities.
• Communicate complex analytical findings and their implications clearly and concisely to both technical and non-technical audiences.

About you

• PhD or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Biomedical Informatics, Engineering, Physics).
• 8+ years of progressive experience in data science, with a significant portion focused on predictive modeling and advanced analytics in healthcare or life sciences.
• Demonstrated expertise in machine learning algorithms and deep learning architectures, including strong practical experience with transformer models (e.g., BERT).
• Proficiency in programming languages such as Python or R, and experience with relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
• Experience working with large-scale, real-world healthcare datasets such as claims data, electronic health records (EHR), or clinical trial data.
• Strong understanding of statistical concepts and experimental design.
• Proven ability to lead complex data science projects from conception to deployment, with a focus on delivering measurable business impact.
• Excellent communication, interpersonal, and leadership skills, with the ability to influence and collaborate effectively across all levels of the organization.