Director - Data Science
First seen by Alion on Sep 25, 2026.
Primary Responsibilities:
Engineering Leadership:
- Lead multiple AI/ML engineering teams in developing scalable ML models, LLM-based solutions, and intelligent automation capabilities across business domains.
- Drive end-to-end delivery of AI products, from ideation through productionization, leveraging modern ML Ops practices at scale.
- Partner with cross-functional teams to determine applicability of AI to business problems.
- Mentor data scientists, AI/ML scientists, and engineers to ensure the delivery of the AI/ML projects, and provide guidance on how to best use specific tools or technologies to achieve the desired results.
- Build and grow a high-performing AI/ML engineering team with roles spanning ML engineers, data engineers, AI architects, and applied scientists.
Technical Leadership:
- Develop and evolve machine-learning methods by adapting model architectures, learning objectives, and feature representations to healthcare-specific data, constraints, and outcomes.
- Establish evaluation frameworks for AI/ML systems beyond accuracy, including reliability, explainability, safety, and real-world impact, in partnership with clinical and business stakeholders.
- Lead failure-mode analysis and iterative method improvement based on model behavior, data drift, and production feedback.
- Define and improve GenAI and LLM-based methods, including RAG architecture, retrieval strategies, grounding techniques, prompt optimization, and hallucination mitigation.
- Establish approaches for controlling and constraining LLM behavior in regulated healthcare settings, including confidence estimation, escalation strategies, and human-in-the-loop workflows.
- Closely collaborate with the Responsible Use of AI (RUAI) team to ensure that the delivered solutions are compliant with the company policies and standards.
- Implement governance aligned with responsible AI principles and regulatory frameworks (HIPAA, CMS), including automated guardrails and model observability.
- Lead value realization efforts, ensuring clear KPIs for business outcomes, quality, and operational performance.
Required Qualifications:
- Masters degree in Computer Science, Math, Statistics, or a related field and 15+ years experience OR a PhD in Computer Science or a related field and 5+ years experience.
- 3+ years experience focused on AI/ML/NLP solutions delivery.
- 5+ years leading AI/ML initiatives end-to-end, including method definition, evaluation, production deployment, and ongoing improvement.
- Experience in implementing AI/ML and/or NLP solutions.
- Strong hands-on background in Python and AI/ML frameworks (PyTorch, TensorFlow), and ML lifecycle tooling (MLflow, AzureML, etc.).
- Demonstrated experience defining or evolving machine-learning methods, including model selection, evaluation, and iterative improvement in production systems.
- Experience developing and deploying data pipelines, machine learning models, or applications on cloud platforms (e.g., Azure, AWS, Databricks, AzureML).
- Experience with Gen AI solution pipelines (e.g., RAG) and Large Language Modeling and Transformer Architectures (e.g., BERT, GPT, etc.).
- Ability to clearly explain AI/ML methods, tradeoffs, and results to technical and non-technical stakeholders.
- Strong understanding of responsible AI, model governance, and regulatory requirements in healthcare.
- Proven understanding of mathematical foundations of machine learning, including statistics, linear algebra, and computer science.
Preferred Qualifications:
- PhD in Computer Science, Mathematics, Statistics, or a related discipline.
- Experience in healthcare (AI) and experience developing AI/ML systems in healthcare or other regulated industries.
- Experience working with cross-functional and distributed teams in a global and diverse environment.
- Experience in establishing AI/ML best practices, standards, and ethics.
- Working knowledge of Software Development tools and practices including DevOps and CI/CD tools (e.g., Git, Jenkins, Docker, Kubernetes, etc.).
- Security and vulnerability management (package scans, remediation).
- Familiarity with data versioning tools (Delta Lake, DVC, LakeFS, etc.).
- Experience with model observability tools for insights into the behavior, performance, and health of your deployed ML models (tracking, alerting, compliance monitoring, etc.).
Skills
Data Science, Artificial Intelligence, Machine Learning, NLP, Python, Data Analytics