Data Science Tech Lead/AI/ML Lead-Remote

Role: Data Science Tech Lead/ AI/ML Lead



Location: Remote

What You'll Do

• Own the problem & the outcome
• Own the initiative end to end - from framing the problem to shipping a solution that measurably improves therapy persistence and intervention timing.
• Partner with clinical product, operations, and business stakeholders to identify requirements, translate ambiguous clinical goals into a concrete technical problem statement, and define what success looks like.
• Refine the modeling target beyond "predict any adverse event" toward the outcomes that matter - therapy discontinuation, serious/clinically meaningful events, and intervention timing.
• Design the solution
• Lead solution design for the full ML lifecycle: data sourcing, feature engineering, modeling approach (including time-to-event / survival framing), evaluation, explainability, and deployment into clinical workflows.
• Make sound build decisions across per-product modeling, concomitant-therapy effects, class imbalance, noisy labels, and point-in-time correctness.
• Design for explainability and clinician trust - every prediction should be interpretable and actionable.
• Drive delivery & lead the team
• Break the work into iterative, demonstrable deliverables - prove value early (e.g., descriptive insights and a risk report) before full workflow integration.
• Define milestones and roadmap, run daily standups, remove blockers, and hold the team and yourself accountable to committed dates.
• Build the solution hands-on alongside data scientists and engineers, set the technical quality bar, and mentor/develop the team.
• Manage stakeholders & navigate the organization
• Manage expectations proactively - communicate what's feasible now vs. later, surface risk early, and renegotiate scope rather than silently slipping.
• Navigate cross-team and political boundaries delicately, including dependencies on data-owning teams you don't control; build trust, escalate constructively, and keep relationships intact.
• Communicate exceptionally well to both technical and non-technical audiences, including delivering honest assessments when the data or timeline demands it.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Core Skills & Technologies

• Python
• Machine Learning
• Deep Learning
• AI / GenAI
• AWS
• Databricks
• Spark
• Survival Analysis
• Model Explainability
• MLOps
• Healthcare Data
• Solution Design