Director Data Science

Job ID: BSWHUS26016398EXTERNALENUSRequisition: 26016398Company: Baylor Scott & White HealthLocation: Dallas, Texas, United StatesEmployment type: full timeCategory: TechnologyIndustry: HealthcareLevel: DirectorShift: Day JobPosted: 2026-10-17

JOB SUMMARY

The Director of Data Science leads BSWH's enterprise data science and advanced analytics portfolio. The role converts business, clinical, customer, and operational priorities into measurable analytical products, including predictive, prescriptive, optimization, experimentation, machine learning, and AI capabilities. The Director is accountable for analytical approach selection, model and experiment evaluation, transition to production with AI Engineering, post-launch monitoring, adoption, and evidence of value.

Within the Data & Knowledge function, this leader establishes the data science operating model, prioritizes investment and capacity, and builds a high-performing team. The Director maintains a portfolio of opportunities with clear value hypotheses, success measures, dependencies, risks, and decision points, and recommends when to build, buy, partner, defer, or retire a capability.

This role works closely with Enterprise Data, Ontology & Knowledge Products, AI Engineering, Product, Digital, Clinical, Operations, Governance, Privacy, Security, and other partners. These partners retain accountability for their respective product, platform, source-data, policy, and clinical approval decisions. Where data science supports clinical or patient-facing decisions, the Director ensures that the intended use is appropriately validated, governed, and implemented with clinical and operational leaders.

This position can be based in our administrative building in Dallas, Texas or mostly remote with some travel required.

ROLE ACCOUNTABILITIES

Portfolio Strategy and Prioritization
- Define and execute the enterprise data science roadmap in alignment with BSWH strategic priorities, the AI use-case portfolio, and measurable value opportunities.
- Establish a consistent intake and prioritization process that assesses expected value, feasibility, data readiness, risk, dependencies, capacity, and time to impact.
- Maintain a portfolio view for major initiatives, including the problem owner, value hypothesis, success measures, stage, decision points, dependencies, and retirement criteria.
- Recommend staffing, funding, vendor, and build-or-buy decisions for data science capabilities and analytical products.
- Set standards for model development, experimentation, documentation, validation, monitoring, and lifecycle management.

Analytical Methods, Machine Learning and AI Enablement
- Select statistical, machine learning, optimization, simulation, GenAI, or agentic AI methods that fit the problem, intended use, data quality, risk, and healthcare context.
- Lead the development of predictive, prescriptive, forecasting, risk, recommendation, personalization, propensity, optimization, and decision-support capabilities across priority domains.
- Guide analytical design and evaluation for generative and agentic AI use cases, including task success, groundedness, safety, human escalation, and operational fit where applicable.
- Partner with AI Engineering and Product teams to define interfaces, deployment requirements, monitoring expectations, service levels, and handoffs for production use.
- Ensure analytical capabilities are reusable, interpretable where appropriate, maintainable, and designed with clear human oversight and intended-use limitations.

Model Evaluation, Experimentation and Value Measurement
- Define pre-deployment and post-deployment evaluation frameworks that cover technical quality, business value, statistical validity, operational impact, fairness and equity, reliability, safety, and customer or clinical outcomes.
- Lead test design, measurement strategy, causal inference, A/B testing, experimentation, and impact evaluation where appropriate.
- Establish success criteria and monitoring measures for model performance, subgroup performance, calibration, bias, drift, adoption, cost, latency, and workflow fit.
- Define requirements for model documentation, model inventory, validation evidence, monitoring thresholds, incident escalation, change control, and retirement.
- Require each major initiative to have a value hypothesis, adoption plan, outcome measures, and a post-launch review or value achievement plan.
- Partner with clinical leaders and appropriate review bodies when models affect care delivery, clinical decisions, patient safety, or patient-facing experiences.

Cross-Functional Delivery and Decision Support
- Translate ambiguous business, clinical, customer, and operational problems into analytical opportunities, delivery plans, and measurable outcomes.
- Define working agreements and handoffs with Product, Enterprise Data, Ontology & Knowledge Products, Data Engineering, AI Engineering, Governance, and domain leaders so ownership is clear throughout the delivery lifecycle.
- Partner with Enterprise Data and Ontology & Knowledge Products to ensure analytical work uses reliable data, shared definitions, appropriate semantic context, and documented assumptions.
- Communicate analytical strategy, tradeoffs, risks, performance, adoption, and value to executive and cross-functional stakeholders.
- Advise leaders when a simpler analytical, operational, or process solution is more appropriate than a complex model or AI capability.

Team Leadership and Operating Model
- Build, lead, and develop a high-performing team of data scientists, decision scientists, machine learning practitioners, and analytical leaders.
- Define team structure, role expectations, staffing needs, career paths, technical review practices, succession plans, and capability development priorities.
- Establish operating rhythms for intake, prioritization, technical review, delivery planning, capacity management, and executive reporting.
- Coach team members on technical quality, stakeholder engagement, communication, responsible AI, and measurable impact.
- Manage the function budget and external partners when applicable, and create a culture of rigor, accountability, curiosity, and continuous learning.

OPRATING SCOPE AND INTERFACES

- The Director is accountable for data science methods, analytical portfolio priorities, evaluation standards, data science capacity, and evidence of analytical value.
- Product and domain leaders own the business or clinical problem, workflow design, operational adoption, and ongoing process ownership.
- Data Engineering and AI Engineering own data pipelines, platforms, model serving, runtime reliability, and production operations; the Director defines analytical requirements and acceptance criteria with those teams.
- Data and Analytics Governance, Privacy, Security, Compliance, Legal, and clinical leaders retain formal policy, risk, privacy, security, and clinical approval responsibilities.

KEY SUCCESS FACTORS

- Proven ability to lead an enterprise data science portfolio and deliver measurable business, customer, clinical, or operational outcomes.
- Technical judgment across statistical modeling, machine learning, experimentation, causal inference, optimization, decision science, and AI methods.
- Experience moving analytical capabilities from discovery through validation, production adoption, monitoring, improvement, and retirement.
- Strong understanding of model risk, evaluation, responsible AI, fairness and equity, privacy, security, and appropriate use of sensitive healthcare data.
- Demonstrated ability to define value hypotheses, measurement plans, adoption strategies, and post-launch outcome reviews.
- Ability to influence senior stakeholders and explain complex analytical concepts, tradeoffs, risks, and results in practical language.
- Strong talent leadership, including hiring, coaching, team design, capability development, performance management, and succession planning.
- Comfort leading through ambiguity and operating in a matrixed, regulated, high-trust environment.

PREFERRED QUALIFICATIONS

Education
- Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, Operations Research, Public Health, Biomedical Informatics, or a related field.
- Master's degree or PhD preferred.

Experience
- 12+ years of experience in data science, machine learning, advanced analytics, applied statistics, decision science, or related disciplines.
- 5+ years of leadership experience, including direct leadership of data science, analytics, machine learning, or related teams.
- Experience developing and operationalizing data science solutions that influence business, customer, clinical, or operational decisions.
- Experience defining model evaluation standards and partnering with engineering teams on deployment, monitoring, observability, continuous improvement, and retirement.
- Healthcare, digital health, health insurance, life sciences, financial services, or another regulated-industry experience preferred.
- Experience supporting AI-enabled products, GenAI or agentic AI systems, digital customer experiences, clinical decision support, operational optimization, or enterprise workflow automation preferred.
- Experience building reusable analytical capabilities that can be applied across multiple business domains and use cases.
- Strong executive communication skills and the ability to present strategy, tradeoffs, risks, outcomes, and investment recommendations to senior leaders.

Technical Expertise
- Strong foundation in statistical modeling, machine learning, predictive analytics, experimentation, causal inference, optimization, and model evaluation.
- Working knowledge of modern data science tools and programming languages such as Python, R, SQL, notebooks, machine learning libraries, and cloud-based analytical environments.
- Ability to evaluate data quality, feature readiness, model performance, interpretability, bias, drift, reliability, fairness, and operational fit.
- Understanding of how models are deployed, monitored, integrated, maintained, and governed in production technology environments.
- Familiarity with LLM evaluation, retrieval-augmented generation, agentic workflow evaluation, personalization, recommendation systems, forecasting, and decision-support methods preferred.
- Strong understanding of privacy, security, responsible AI, governance, and appropriate data use considerations for sensitive healthcare data.

MINIMUM REQUIREMENTS

- Bachelor's degree or an equivalent combination of education and experience.
- 7+ years of progressive experience in data science, advanced analytics, machine learning, applied statistics, decision science, or a related field.
- Experience leading a data, analytics, technology, clinical, or operational team, program, or portfolio.