Senior Data Science/ML leadership role responsible for building and maturing an enterprise Data Science capability using ML, AI, and advanced customer analytics to improve the patient/customer journey. The Director will lead a 7-person Data Science team, drive models from concept through production, and establish standards for model development, experimentation, and lifecycle management. Requires 12+ years of Data Science/ML experience, 5+ years of team leadership, strong customer analytics and personalization expertise, and hands-on technical credibility as a player-coach.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Director, Data Science
Work Model: Fully Remote
Target Base: $245,000–$265,000
Bonus: 20% annual bonus
Travel: Dallas approximately once per quarter at most
Work Authorization: U.S. Citizens / Green Card holders only — no sponsorship
Cannot hire candidates residing in: California, New York, Hawaii, North Dakota, Oregon, Rhode Island, Washington, or Wyoming.
What They Need
This is a senior Data Science/ML leadership position responsible for building and maturing an enterprise Data Science capability while using ML, AI, and advanced customer analytics to materially improve the patient/customer journey.
The ideal candidate sits at the intersection of:
Data Science/ML Leadership + Customer Analytics + Segmentation/Personalization + Voice of Customer + Production ML Products.
The Director will inherit and lead a 7-person Data Science team, but they cannot be an executive who has moved too far away from the technology. The hiring team wants a technically credible player-coach who can set strategy, develop the team, challenge technical approaches, and help move Data Science products from concept through production and business adoption.
Core Responsibilities
- Define and execute the enterprise Data Science roadmap
- Lead and develop the existing 7-person Data Science organization
- Build customer segmentation and personalization capabilities
- Lead predictive, recommendation, forecasting, and decision-support modeling
- Use structured and unstructured/Voice of Customer data to identify friction across the customer journey
- Develop ML and AI-enabled analytical products that directly influence customer experiences
- Establish standards around model development, validation, experimentation, monitoring, and lifecycle management
- Drive models beyond POCs into production and measurable business adoption
- Partner closely with Data Engineering, AI Engineering, Product, Digital, Clinical, and Operational teams
- Define how model performance and business impact should be measured
- Establish strong experimentation, A/B testing, causal inference, and impact-evaluation practices
- Serve as a senior advisor to executives on where Data Science and AI can create meaningful value
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Must Haves
- 12+ years of Data Science, Machine Learning, Advanced Analytics, Applied Statistics, or related experience
- 5+ years leading Data Science, ML, or advanced analytics teams
- Currently or recently leading a genuine Data Science/ML function
- Strong customer analytics/customer experience background
- Hands-on experience with customer segmentation and personalization
- Predictive and recommendation modeling experience
- Experience working with unstructured data and/or Voice of Customer data
- Proven history taking ML/Data Science products into production
- Strong Python, SQL, statistical modeling, and Machine Learning foundation
- Experience with model evaluation, monitoring, drift, interpretability, and lifecycle management
- Strong experimentation and measurement knowledge
- Ability to operate as a technical player-coach
- Strong executive communication and cross-functional leadership
- Bachelor's degree in a relevant quantitative/technical discipline
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Strongly Preferred
- LLM / GenAI experience
- Customer-facing AI products
- Hyperscaler/cloud personalization capabilities
- Recommendation systems
- Causal inference and sophisticated experimentation
- Semantic models, ontologies, or knowledge graphs
- Responsible AI/model governance experience
- Healthcare, digital health, health insurance, life sciences, financial services, or another regulated environment
- Master's degree or PhD
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