• Develop and tune generative/agentic AI solutions aimed to solve highly complex problems within the delivery of healthcare or health plan services. • Establish comprehensive tracking of experiments to manage the iterative process of building and testing models ensuring that AI solutions fulfill business requirements. • Provide statistical rigor to these evaluations via the design of controlled tests to measure the impact of changes to the solution. • Form and leverage strong collaborations with AI engineers to scale solutions for production grade performance. • Develop and implement complex agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision-making capabilities. • Solve new business problems by reaching, designing, building, and validating complex or novel machine learning models. • Conduct feature engineering and model optimization. • Manage the complete lifecycle of machine learning models from conception to deployment while following SDLC and responsible AI practices. • Consult with senior and executive level business leaders to scope, model, and recommend AI/ML, technical, or analytic solutions to highly complex problems within healthcare. • Build and ensure consensus with stakeholders regarding the feasibility, tradeoffs, and delivery of recommended solutions. • Research, recommend, and apply causal techniques (RCT, DiD, PSM, causal graph models, counterfactual reasoning, etc.) to estimate treatment effects to quantify or validate business benefit of complex health plan activities. • Explain business impact to program owners and leadership using approaches that are meaningful to those audiences. • Partner with program owner in the promotion or publications of results when applicable. • Participate in code peer reviews and quality assurance testing. • Troubleshoot issues as they arise to solve problems independently and collaboratively. • Serve as the technical subject matter expert to establish best practices for the team and help coach the team via formal and informal initiatives. • Maintain regular contact with customers through development cycle to ensure each step of implementation tracks customer’s needs. • Lead analytics projects applying a working knowledge of Agile and SCRUM project methodologies.

Role Description We are seeking an experienced Senior Data Governance Lead to drive the next phase of our data governance evolution. This role is ideal for a hands-on leader who can translate abstract governance concepts into clear taxonomies, measurable data rules, and enforceable quality standards that scale across the organization. You will help to lead a taxonomy and rules transformation—establishing shared definitions, structured classification systems, and outcome-driven data quality rules that directly improve data trust, usability, and business impact. This role partners closely with data, product, engineering, operations, and analytics teams to embed governance into how data is designed, produced, and consumed—not as overhead, but as enablement. This role is critical to building long-term data governance muscle across the organization by equipping teams with practical frameworks, guidance, and repeatable ways of working. Responsibilities: - Lead the design and governance of an enterprise taxonomy and data rule framework, including business terms, entities, products, and classification hierarchies that support consistent data use. - Translate business needs into clear, objective, and measurable data and quality rules that can be implemented, tested, and monitored. - Develop governance frameworks that connect taxonomy → business rules → data quality standards → measurable outcomes. - Partner with data engineering, analytics, and product teams to embed governance requirements into data pipelines, tooling, and workflows. - Establish standards for rule authoring, validation, conflict detection, and lifecycle management. - Drive clarity and consistency in definitions, metadata, and documentation across domains. - Support the creation of data quality metrics, thresholds, and reporting aligned to business use cases. - Coach and enable data owners, stewards, analysts, and associates to apply governance standards confidently and consistently. - Contribute to governance operating models, playbooks, and training materials designed for both technical and non-technical audiences. - Act as a thought partner on governance best practices, tooling needs, and maturity progression. Success Measures: - Taxonomy Adoption & Clarity – Demonstrated reduction in ambiguity and rework through consistent use of approved taxonomies and definitions. - Rule Effectiveness – Clear, testable data and quality rules that lead to measurable improvements in data accuracy, consistency, and trust. - Scalability of Governance – Frameworks and standards that teams can apply independently without constant governance intervention. - Data Quality Improvements – Observable improvements in key data quality indicators tied to governed datasets. - Stakeholder Enablement – Positive feedback from teams on the usability, clarity, and practicality of governance guidance. - Operationalization – Governance artifacts (taxonomy, rules, standards) actively used in production workflows rather than existing only as documentation. Qualifications - 5–10 years of experience in data governance, data management, data quality, analytics, or data operations. - Proven experience designing or implementing taxonomy frameworks, classification systems, or business term definitions. - Strong ability to translate complex business concepts into structured, objective rules. - Experience working cross-functionally with data engineering, analytics, product, and operational teams. - Demonstrated success moving organizations from ad hoc or narrative governance toward structured, standards-based approaches. - Familiarity with data quality dimensions (e.g., accuracy, completeness, timeliness, consistency) and how to operationalize them. - Knowledge of or certification in DAMA / DMBOK is a strong plus. - Comfort operating in environments with evolving maturity and ambiguity. - Excellent written and verbal communication skills.

• Design, build, and deploy LLM-based solutions on Snowflake, leveraging Cortex AI functions (e.g., Snowflake LLM inference, fine-tuning, embeddings). • Create custom LLM pipelines using Snowpark ML and external models integrated through Snowflake. • Build and operationalize machine learning models, including NLP, predictive modeling, generative AI, and recommendation systems. • Develop and apply prompt engineering, RAG pipelines, and semantic search patterns using Snowflake features and Vector Embeddings. • Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling using Snowflake & Snowpark Python. • Deploy, monitor, and optimize model performance directly within the Snowflake environment. • Integrate Snowflake ML solutions with cloud platforms (GCP) and downstream applications. • Work with business stakeholders, product teams, and analytics leaders to identify opportunities for AI-driven transformation. • Deliver high-quality, interpretable insights and communicate complex technical results to non-technical audiences. • Partner with data engineers, architects, and analysts to build end-to-end Snowflake-native AI solutions.

• The person in this position is responsible for the development of program logic models, supporting measurement plans, and impact metrics. • They will partner with program teams to execute strategies, aligning on measurement plans, and evaluating programs that support the ASPCA’s goals to improve the lives of animals. • They will conduct in-depth studies across ASPCA programming to generate credible measures of the programming’s current performance and to determine what efforts are most effectively and efficiently resulting in desired impacts. • As a member of the Strategy team, the Director, IMDS will lead the design and development of program learning logic models and program evaluations in collaboration with ASPCA program teams, subject matter experts, external partner organizations and other external stakeholders. • In addition to strong program evaluation, research, and analytical skills, this position requires an individual with a collaborative work style, experience in project management, and the ability to lead teams in a matrix structure, often without direct authority.