• Develop and implement algorithms for navigation, positioning, and motion estimation • Work with sensor data (e.g., GPS/GNSS, IMU) to build reliable models • Design and apply sensor fusion techniques to combine data sources • Implement and optimize filtering and estimation algorithms (e.g., Kalman Filters) • Analyze large datasets to identify patterns and performance improvements • Collaborate with engineering teams to integrate models into production systems • Validate models through simulations and real-world data testing • Document methodologies, assumptions, and results for stakeholders

• Seeking a Senior Data Scientist to support a proof-of-concept demonstration using natural language processing and other machine learning methods to improve the intake process. • Oversee and support the development, implementation, and testing of statistical models, integration of NLP, and refinement and testing of the prototype. • Work closely with State stakeholders and technical team members to ensure the quality of the results and that the derived methods are transparent, statistically sound, relevant, and documented. • Establish a framework for the execution of technical tasks within the proof-of-concept. • Identify critical milestones related to information, receipt of data, testing, and delivery. • Identify key risk factors and means of mitigation. • Lead the development of a new intake process that leverages natural language processing and other machine learning algorithms. • Document the final approach for transparency. • Create the framework for the design review process.

• Represent CenterWell in Enterprise Data Governance forums; secure alignment and decision rights • Publish CenterWell governance playbook; establish stewardship, ownership, access controls, and issue resolution • Maintain CenterWell data catalog, business glossary, KPI dictionary, lineage, and quality standards for cross-CenterWell assets • Partner with IT on unified data/platform strategy (UDAP) and guardrails for advanced self-service such as agentic, self-service analytics • Define standards and lifecycle for cross-CenterWell data products and feature stores; implement build-once approaches • Enable adoption through training, documentation, and support scaffolding • Develops and owns the cross-CenterWell interoperability strategy, enabling CenterWell businesses to access insights about their members both across the org, as well as external data from third party sources • Owns the interoperability integration strategy for standardly available data products for CenterWell stakeholders • Stand up intake, triage, and prioritization for cross-CenterWell data requests; reduce duplicative effort • Deliver common reporting and product/process monitoring; support pipelines for CenterWell-wide analytics • Jointly define, size, and prioritize predictive models and machine learning capabilities and backlog that maximizes value to CenterWell • Maintain deep technical expertise in advanced data science and AI (i.e., predictive modeling, NLP, classification) to upskill teams across Centerwell through new opportunities and approaches • Implement MLOps across CenterWell, in partnership with Enterprise teams, and responsible AI (privacy, fairness, explainability); partner with Legal, Compliance and Security • Partner with Enterprise teams to deliver enterprise-ready models, influence cross-enterprise platforms (ex – Florence), and integrate workflows to drive adoption and outcomes • Establish clear interfaces, RACI, and escalation protocol across CenterWell D&A Central, BUs, and Enterprise • Champion the elevation of data science capabilities across CenterWell, providing upskilling and reusable data assets that enables distributed teams to focus on faster insight generation and more sophisticated, novel analytics • Build a community of practice and coach leaders/ICs; attract and grow top D&A talent

• Apply statistical analysis and experimentation to driver customer growth, optimization and data information business decisions • Develop, clean and analyze datasets using tools like SQL, Python or R to generate actionable insights • Partner with cross functional teams to translate business problems into data driven solutions and measurable outcomes • Ensure data quality, integrity, and consistency through data validation, quality monitoring, documentation, and adherence to data governance standards. • Support data governance initiatives including master data management, metadata documentation and enrichment in the Data Catalog, and data classification to enable responsible data access, privacy compliance, and trusted data assets. • Leverage AI and machine learning techniques to enhance predictive insights, automate processes, and responsibly scale data driven decision making