Executive Director, Data, Machine Learning & AI

This role is responsible for reliably delivering the company's initiatives in Big Data, Data Science, Machine Learning, and the safe, effective use of LLMs. Success requires close collaboration across teams, along with clear, kind, and consistent communication. The role will offer frequent opportunities to mentor and develop others. As the company embarks on a significant, multi-year, cross-disciplinary data investment, fast and dependable execution on these high-priority initiatives is essential.

What You Will Do

• Set enterprise data & AI strategy in coordination with Leadershipthat aligns to company and product strategies; define a 2-3 year roadmap for data platforms, analytics, ML/AI capabilities (including GenAI), and business value realization.

• Deliver on the data platform(data lake/lakehouse, warehouses, streaming) evolution; ensure scalability, reliability, cost efficiency, and performance across our cloud environments. Architecture will be owned by the Architecture team, so a close working relationship is necessary.

• Establish and chair data & AI governance(policies, standards, data contracts, model governance), balancing innovation with risk, privacy, and regulatory compliance (e.g., HIPAA, GDPR/CCPA)

• Operationalize MLOps/LLMOps: implement reproducible model lifecycle management (experimentation, approval, deployment, monitoring, drift/evals, rollback), feature stores, CI/CD, and automated observability.

• Drive GenAI adoption responsibly: identify high value use cases, build/reuse LLM platforms (RAG, vector search), set prompt/eval standards, safeguard IP/PHI, and manage content/usage policies and human in the loop controls. Much of this workstream will be in tight coordination with the office of our CISO.

• Deliver measurable business outcomesvia data products and ML/AI solutions; prioritize the portfolio, define KPIs/OKRs with business owners, and track ROI, adoption, risk, and quality.

• Lead a multidisciplinary organization(data engineering, platform, analytics, data science/ML, data governance, AI product) with clear operating mechanisms, talent strategy, and succession plans.

• Partner with Security, Legal, Compliance, and Riskto implement privacy by design, model risk management, third party risk, and audit readiness; ensure encryption/IAM, data retention, and lineage are enforced.

• Advance data quality: institute golden sources, data stewardship, data quality SLAs, and remediation workflows across domains.

• Embed architecture standards and patterns: event-driven data, streaming (e.g., Kafka/Kinesis), APIs, data mesh/data product patterns, and zero ETL/ELT best practices.

• Manage vendor and partner ecosystem: evaluate/contract platforms and model providers, negotiate commercial terms, and ensure interoperability and exit options.

• Own financials for the function: plan budgets, forecast run/transform costs, optimize cloud spend (FinOps), and reinvest savings into innovation.

• Champion change management: communicate vision and progress to executives and broader teams; create communities of practice and playbooks to scale adoption.

• Ensure resilience and continuity: architect for HA/DR, data protection, backup/restore, and incident response for data and AI systems.

• Represent the company externallywhere appropriate (industry forums, academia, standards bodies) to attract talent and shape best practices.

What You Will Have

• Bachelor's or Master's Degree in a related field preferred

• 15 years of related experience with 7+ years in leadership/team management

• Data Architecture:Lake/lakehouse and warehouse design; dimensional/semantic modeling; data product architecture; data mesh and data contracts.

• Cloud Data Platforms:Deep experience with one or more of AWS (S3, Glue, EMR, Redshift, Lake Formation), Azure (ADLS, Synapse, Fabric), Snowflake, and Databricks; multicloud patterns and connectivity.

• Streaming & RealTime:Event-driven design, CDC, and stream processing (e.g., Kafka/Kinesis, Spark Structured Streaming, Flink); lowlatency serving layers.

• Data Engineering Excellence:ELT/ETL frameworks, orchestration (Airflow/Databricks Workflows), metadata/lineage (OpenLineage), partitioning, performance tuning, and cost optimization.

• Analytics & BI:Metric stores/semantic layers, governed selfservice, embedded analytics, and A/B testing/experimentation frameworks.

• Machine Learning Fundamentals:Feature engineering, model development for classical ML and deep learning, offline/online feature stores, model packaging, and reproducible experimentation.

• MLOps/Model Governance:CI/CD for ML, approval gates, automated evaluations, drift detection, bias/fairness testing, explainability (e.g., SHAP), model catalog/registry, and monitoring/alerting.

• Generative AI & LLMs:Model selection (open/hosted), prompt engineering, RAG architectures, vector databases, policy/guardrails, redteaming, hallucination mitigation, and LLM evaluation metrics.

• AI Safety, Privacy & Compliance: Privacybydesign, PHI/PII handling, HIPAA and global privacy regulations (GDPR/CCPA), dataset provenance, copyright/IP controls, and secure isolation for training/inference.

• Security for Data & AI:IAM/leastprivilege, key management and encryption (at rest/in transit), secrets management, network segmentation, and secrets scanning; secure supply chain for data/ML artifacts.

• Data Governance & Quality:Stewardship operating model, profiling and rules, DQ SLAs, remediation workflows, golden sources/MDM, and business glossary/catalog (e.g., DataHub/Collibra/Purview).

• AI/ML Product Management: Usecase discovery, value hypothesis and ROI modeling, stakeholder alignment, roadmapping/prioritization, and change management for adoption.

• SRE/Platform Reliability:SLOs/SLIs for data and ML services, capacity planning, HA/DR patterns, cost/perf telemetry, and incident management.

• Integration & APIs: Contract first API design, data services for operational systems, and secure interoperability with SaaS/ISVs.

• Financial & Commercial Acumen:TCO modeling, FinOps for data/AI workloads, license/subscription governance, and scalable chargeback/showback.

• Leadership & Talent:Organization design, hiring and coaching for data/ML/AI roles, vendor/partner management, and building communities of practice.

• Communication & Influence:Executive storytelling with data, risk/benefit framing, and the ability to align diverse stakeholders on standards and tradeoffs.