Data Science Tech Lead

Location: RemoteNeed 10+ years experience candidates. Position on W2Core Skills & Technologies PythonMachine Learning Deep LearningAI / GenAIAWSDatabricksSparkSurvival Analysis Model ExplainabilityMLOpsHealthcare Data Solution Design

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.