Principal Data Scientist

Job Title: Principal Data Scientist

Contract: 12-month contract with potential for extension/conversion

Fully Remote, must be comfortable working EST Hours

Pay: Bid rate position! Manager is open on pay rate at long as candidate meets qualifications

PLEASE NOTE THE QUALIFICATIONS:

• PhD or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Biomedical Informatics, Engineering, Physics).
• MUST HAVE: Experience with Disease onset or prognostic modeling
• 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.

Job Description:

• We are seeking an experienced and visionary Principal Data Scientist to lead our efforts in developing advanced predictive models and AI solutions for healthcare.
• E.D.G.E. Team (Emerging Disruptive Growth Exploration) conducts cutting-edge research in health care and incubates data-driven digital and non-digital solutions which aim to improve a person’s health outcomes, the lives and ability for families to support and care for their loved ones, clinicians’ experience, and to reduce health care costs.
• 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.
• 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 Client approaches into our predictive modeling capabilities.
• Communicate complex analytical findings and their implications clearly and concisely to both technical and non-technical audiences.