Senior Data Scientist

Job Summary

Surescripts serves the nation through simpler, trusted health intelligence sharing, in order to increase patient safety, lower costs and ensure quality care. The Senior Data Scientist will play a pivotal role in developing and implementing data‑driven solutions across various business functions, collaborating with cross‑functional teams to analyze complex data sets, derive actionable insights, and drive strategic decision‑making. The Senior Data Scientist will design, deliver, and optimize powerful insights and visuals with advanced analytics and AI, including effective use of AI assistants within development tools to accelerate planning, solution design, implementation, and documentation.

• Data Exploration: Explore and preprocess data from various sources, measuring and ensuring data quality and integrity. Enrich data with external, auxiliary, or commercial data sets to enhance suitability for monetization or AI/ML.

• Advanced Analytics: Apply statistical and machine learning techniques to large datasets and develop predictive models, deep learning algorithms, and frameworks using tools such as Tensor Flow and PyTorch. Leverage model farms and other AI repositories for development of innovative data processing pipelines.

• Model Development: Design, build, and validate predictive models to solve business problems. Provide team leadership to ensure provenance and traceability in MLOps development cycles.

• Data Visualization: Create compelling visualizations to communicate findings and insights to stakeholders.

• Collaboration: Work closely with business leaders, product managers, data scientists, and engineers to translate business requirements into analytical solutions.

• AI‑Assisted Planning & Design: Use AI assistants to accelerate requirements clarification, solution options, and technical design; convert outputs into reviewed artifacts (design docs, user stories, test plans).

• AI‑Assisted Implementation: Use AI coding assistants to draft code/queries/notebooks/pipelines; perform human review for correctness, security, performance, and maintainability; follow IP/licensing and data‑use policies.

• QA Oversight: Define acceptance criteria, test strategy, and validation methods (data checks; model metrics and bias/robustness as applicable); partner with engineering/QA on regression coverage and release readiness.

• AI‑Teaming & Refinement: Iteratively refine solutions using prompt engineering, grounding/source practices, and evaluation rubrics; document prompts, assumptions, and decisions for repeatability.

• Mentorship: Provide guidance and mentorship to other data scientists, analysts, and engineers within the team; serve as a mentor/role model, imparting analytic knowledge, experience, and skills to staff at all levels.

• Continuous Learning: Stay abreast of industry trends, emerging technologies, and best practices in data science.

• Evangelism: Evangelize within the company the capabilities and opportunities that data science and advanced analytics empower toward corporate goals.

• Data Quality Assurance: Question, validate, and perform QA of the data for integrity and consistency to support ongoing data quality assessment and improvement initiatives.

• Privacy & Security Compliance: Understand all applicable data privacy and security laws, rules, regulations, and contractual restrictions, and follow all Surescripts data governance and data usage rights policies and procedures.

• Access Documentation: Document access and use requirements for advanced analytics data/reporting products and support definitions of report templates and specifications.

• Communication: Demonstrate ability to communicate complex analytic results in a clear and concise fashion to sponsors/clients at all levels and to audiences of all sizes.

• Executive Reporting: Summarize and synthesize large bodies of work down to the essential elements and convey those results effectively to senior leaders.

• Effective
  
  Collaboration:
  
  Effectively communicate with and engage colleagues at all levels of the organization.

• Delegation: Effectively delegate responsibilities to the appropriate people and levels.

• Networking: Develop internal and external…