Note: The job is a remote job and is open to candidates in USA. Ket Software is seeking a Data Science Tech Lead to own and deliver a machine learning solution focused on adverse events, therapy discontinuation, and intervention timing for specialty patients. The role will shape the problem with clinical and business stakeholders, design the end-to-end ML solution, lead data scientists and engineers, and drive delivery into clinical workflows.
Responsibilities
- 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
Skills
- 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
- Python
- Machine Learning
- Deep Learning
- AI / GenAI
- AWS
- Databricks
- Spark
- Survival Analysis
- Model Explainability
- MLOps
- Healthcare Data
- Solution Design
- A leader who takes ownership and drives outcomes without waiting to be told
- Self-motivated and results-driven, comfortable with ambiguity and tight timelines
- An exceptional communicator across technical and clinical/business audiences
- Skilled at breaking big, fuzzy asks into clear iterative deliverables
- Diplomatic and effective at navigating organizational and political boundaries
- Deeply technical yet grounded in business value and patient impact
- 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
Company Overview
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