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 outcomeOwn 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 solutionLead 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 teamBreak 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 organizationManage 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 PythonMachine LearningDeep LearningAI / GenAIAWSDatabricksSparkSurvival AnalysisModel ExplainabilityMLOpsHealthcare DataSolution Design