Vice President, Data Strategy

Position Overview:

We are seeking a visionary Vice President of Data Strategy to lead our enterprise data strategy and accelerate our growth as an AI-first healthcare organization. This executive will own the end-to-end data lifecycle—from infrastructure and governance to advanced analytics, machine learning, and generative AI—and translate that foundation into measurable clinical, operational, and financial outcomes. 

The ideal candidate is equal parts strategist, technologist, and builder: someone who has scaled modern data platforms, deployed production AI systems, and navigated the unique complexity of healthcare data. You will partner with product, engineering, clinical, and commercial leadership to ensure data and AI are competitive differentiators, not back-office functions. 

Location: Our strong preference is for candidates to be within commuting distance of Atlanta or Philadelphia. We have team members in both locations who visit our office weekly on Wednesdays. However, we will also consider applicants in other U.S. locations in a remote capacity.

Reports to: Chief Technology Officer

As the VP of Data Strategy at Trella, you will lead:

Strategy & Vision 

• Define and execute a multi-year data and AI roadmap aligned to our enterprise strategy with clear investment cases, KPIs, and ROI milestones. 
• Champion an AI-first operating model: embedding machine learning, predictive analytics, and generative AI into products, workflows, and decision-making across the organization. 
• Serve as the executive voice of data, educating senior leadership, and customers on emerging AI capabilities and responsible adoption. 

AI & Machine Learning 

• Build and scale the organization’s AI/ML capabilities, including traditional ML, deep learning, NLP on clinical text, and generative AI / LLM applications (RAG, agentic workflows, fine-tuning). 
• Establish MLOps and LLMOps practices covering model development, evaluation, deployment, monitoring, and retraining at production scale. 
• Participate in an AI governance framework addressing model risk, bias, explainability, clinical safety, and compliance with evolving healthcare AI regulations (HHS, FDA SaMD, HTI-1, state-level AI laws). 
• Evaluate and integrate third-party AI platforms and foundation models while building proprietary capabilities that create durable competitive moats. 

Data Platform & Engineering 

• Own the data stack: cloud data warehouse/lakehouse (Snowflake, Databricks, BigQuery), ELT (dbt, Fivetran), orchestration (Airflow, Dagster), streaming, and semantic layers. 
• Drive data product thinking: treat datasets, features, and models as versioned, documented, discoverable products with named owners and SLAs. 
• Ensure the platform supports real-time analytics, self-service BI, embedded analytics for customer-facing products, and feature stores for ML. 

Analytics & Insights 

• Lead enterprise analytics: product analytics, commercial analytics, clinical outcomes, population health, and financial/operational reporting. 
• Deliver executive dashboards and advanced analytics that directly influence strategy, pricing, product roadmap, and care delivery. 
• Build a high-performance culture of experimentation, A/B testing, and causal inference. 

Governance, Privacy & Compliance 

• Own data governance, master data management, data quality, and lineage across clinical, claims, and operational domains. 
• Ensure full compliance with HIPAA, and SOC 2, and applicable state privacy laws; partner with Security and Legal on data sharing agreements, BAAs, and de-identification standards. 
• Establish policies for Protected Health Information (PHI) use in AI training, prompt engineering, and vendor integrations. 

People & Organization 

• Build, mentor, and retain a world-class team spanning data engineering, analytics engineering, data science, ML engineering, BI, and data governance. 
• Create career frameworks, hiring bars, and a culture that attracts top AI/ML talent in a competitive market. 
• Develop cross-functional analytics partnerships with Product, Engineering, Clinical, Finance, Sales, and Marketing. 

This job might be a fit for you if you have: 

• 10+ years of progressive experience in data and analytics leadership roles, including 5+ years managing multi-disciplinary teams (data engineering, data science, analytics).
• Healthcare industry experience is required: demonstrated track record working with healthcare data such as claims (Medicare, Medicaid, commercial), EHR/EMR data, clinical coding (ICD-10, CPT, HCC, LOINC, SNOMED), HL7/FHIR, and healthcare interoperability standards.
• Deep expertise in HIPAA, PHI handling, de-identification (Safe Harbor, Expert Determination), and healthcare-specific data security and compliance frameworks.
• Proven experience shipping production AI/ML systems at scale.
• Prior hands-on experience with a modern data stack: cloud data warehouses/lakehouses, SQL, Python, dbt, orchestration tools, and at least one major cloud provider (AWS, Azure, or GCP).
• Strong grounding in MLOps/LLMOps, feature stores, model monitoring, vector databases, and retrieval-augmented generation (RAG) architectures.
• Experience owning data governance programs and navigating audits (HITRUST, SOC 2, or equivalent).
• Exceptional executive communication with the ability to translate complex technical concepts for boards, customers, clinicians, and non-technical stakeholders.
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related field; advanced degree strongly preferred.

Preferred Qualifications

• Experience in value-based care, population health, risk adjustment, care management, or life sciences/pharma analytics.
• Prior experience at a healthcare SaaS, payer, provider, HealthTech, or digital health organization.
• Familiarity with CMS data (LDS, VRDC, CCW), commercial claims datasets, or real-world evidence (RWE) data.
• Experience building customer-facing analytics or AI products (embedded analytics, AI copilots, agentic workflows).