Lead Data Scientist will support advanced analytics, predictive modeling, and machine learning initiatives for clinical decision-making in healthcare. Responsibilities include algorithm design and compliance with VA governance standards.
VHA supports and provides medical care for VA’s eligible beneficiaries through the VA health program, which includes VA Medical Centers (VAMCs) and contracted health care networks. The Office of Clinical Informatics within the Office of Health Informatics (OHI), Digital Health Office (DHO) advances the enterprise standard of care and patient experience using improved data, tools, and informatics processes organized around continuously delivering value to its customers. This is achieved through Lean-Agile delivery of clinical practice solutions that support best practice standards for clinical care. OHI is further responsible for ensuring the success of the modernized VA EHR, Oracle Health Millennium/Cerner through continuous exploration, integration, deployment and release on demand of Integrated Health Technology (IHT) solutions. These efforts aim to increase Veterans' access to care and support their active participation in their healthcare. Gritter Francona is looking for a Lead Data Scientist to help support a potential project to assist this objective.
Key Responsibilities- Serves as the technical lead for advanced analytics, predictive modeling, and machine learning initiatives supporting clinical decision-making and operational optimization.
- Designs and validate algorithms for interoperability workflows, clinical informatics systems, and Agile integration reporting.
- Ensures compliance with VA data governance, security, and privacy standards.
Requirements
- MUST HAVE demonstrated expertise in Agile environments (e.g., supporting PI planning, backlog analytics, velocity metrics); Interoperability standards (e.g., HL7, FHIR) and clinical terminology (SNOMED, LOINC); Programming languages (e.g., Python, R) and frameworks (e.g., TensorFlow, PyTorch); Data visualization tools (e.g., Power BI, Tableau) and ETL processes;
- Proven experience integrating clinical informatics systems (VA VistA/CPRS, Cerner Millennium) and validating data migration fidelity
- Must be eligible to obtain a government clearance
- Master's degree in Data Science, Computer Science, Health Informatics, or Statistics
- 10+ years in data science or advanced analytics roles
- 5+ years applying machine learning and predictive modeling in healthcare or federal health IT environments
Benefits
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary & AD&D)
- Paid Time Off (Vacation, Sick & Public Holidays)
- Short Term & Long Term Disability
- Training & Development
Similar Jobs
Artificial Intelligence • Automotive • Computer Vision • Information Technology • Internet of Things • Logistics • Software
Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.
Top Skills:
Computer VisionDeep LearningDetectionEvaluation FrameworksGenerative AiGeospatial AiLabel Quality AssessmentMappingMultimodal AiPerception SystemsSegmentationSimulationStructural Similarity (Ssim)Synthetic DataTemporal ConsistencyVideo Quality Metrics
Consumer Web • Coupons • Healthtech • Social Impact • Pharmaceutical
Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.
Top Skills:
AWSAzureDatabricksEmrExperimentation PlatformsFeature StoreGCPGenerative AiLlmsMlopsModel MonitoringNumpyPandasPostgresPysparkPythonPyTorchRedshiftScikit-LearnSparkTensorFlow
Healthtech
Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.
Top Skills:
Advanced AnalyticsAgileArtificial IntelligenceData VisualizationDeep LearningFeature EngineeringGenerative AiMachine LearningNatural Language ProcessingOptimizationPredictive ModelingStatistical Modeling
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
