Data Science Tech Lead

Core Skills & Technologies

• Python
• Machine Learning
• Deep Learning
• AI / GenAI
• AWS
• Databricks
• Spark
• Survival Analysis
• Model Explainability
• MLOps
• Healthcare Data
• Solution Design, * 7+ years of data science and machine learning experience, delivering models that reached production or drove real business decisions.
• 3+ years leading machine learning or data science projects end to end - from problem framing through deployment.
• 3+ years working directly with business stakeholders and product owners: managing expectations, owning delivery, and driving a high-visibility workstream under tight deadlines.
• Significant solution-design experience for building machine learning systems, not just individual models.
• Strong hands-on expertise in Python and the modern ML/DL ecosystem.
• Deep, practical understanding of machine learning and deep learning - able to choose the right approach and reason about tradeoffs, evaluation, and failure modes.
• Experience with AWS and Databricks for building and deploying data/ML solutions at scale.
• Experience working with healthcare data (and awareness of the associated data-quality, privacy, and governance realities).
• Demonstrated leadership presence: self-motivated, driven to deliver results, and able to earn the confidence of both technical teams and senior stakeholders., * Experience in pharmacy, specialty pharmacy, or clinical/patient-outcomes domains, with working familiarity of the relevant datasets (therapy, dosing, adverse events, discontinuation, claims).
• Familiarity with time-to-event / survival analysis and its application to intervention-timing problems.
• Experience deploying models into clinical or operational workflows with human-in-the-loop decisioning and measurable outcome validation (e.g., controlled rollouts).
• Exposure to Generative AI / agentic approaches and a pragmatic view of where they fit in a regulated setting.
• Experience with data governance, PHI/HIPAA constraints, and model documentation in a regulated environment.
• Familiarity with MLOps practices: feature stores, model monitoring, and reproducible pipelines.