Sr. Data Scientist - Product Development Company - Blumetra Solutions

Designation: Sr. Data Scientist

Work Location: Hyderabad (Hybrid Model)

Experience: 5+ Years

Core Responsibilities

• Multimodal Enrollment Forecasting: Build hierarchical models that forecast "Top-Down" (Country/Study) and "Bottom-Up" (Site-level) enrollment by ingesting real-time screening logs from IRT and site-activation milestones from CTMS.
• Discontinuation & Attrition Modeling: Implement Survival Analysis (Cox-PH, DeepSurv) and RNNs to predict patient dropout probability using longitudinal data from EDC, which serves as the primary driver of "Maintenance Phase" demand.
• Demand vs. Supply Optimization: Develop Monte Carlo simulations or Stochastic Optimization models to determine safety stock levels, balancing the variance between predicted enrollment and actual inventory on hand.
• Dose Titration Logic: Build predictive ML models to anticipate dose escalations or reductions—syncing with IRT dispensing data to ensure the correct kit strength is available at the site before the patient’s next visit.
• Clinical Data Lake Management:
• Architect unified data pipelines that join EDC (clinical outcomes/visit data) with IRT (supply/randomisation data).
• Manage the full ML lifecycle (Tracking, Registry, Serving) to ensure model reproducibility.
• Build resilient, real-time pipelines for monitoring supply-demand signals and triggering automated alerts for potential stock-outs.

Required Technical Expertise

• Systems Integration: Proven experience processing and feature-engineering data from EDC (e.g., Medidata Rave, Veeva) and IRT/RTSM platforms.
• Advanced ML Domains:
• Time-Series: DeepAR, Temporal Fusion Transformers (TFT), or N-BEATS for non-linear recruitment trends.
• Survival Analysis: Expert-level experience modeling "Time-to-Event" data to handle censored patient discontinuation patterns.
• Probabilistic Programming: Experience with PyMC or Gurobi/OR-Tools to solve the "Supply vs. Demand" constraint problem.
• Data Engineering: Expert-level Python, SQL, and distributed computing for processing large-scale, high-velocity clinical datasets.

Clinical Domain Knowledge (Preferred To Have)

• Clinical Systems: Deep understanding of the data schemas within IRT/RTSM (Randomisation/Dispensing) and EDC (Patient Visits/Adverse Events).
• Supply Dynamics: Understanding of "Initial Seeding," "Trigger-based Resupply," and "Dose Titration" within a global trial context.
• Regulatory Context: Experience working within GxP / CFR Part 11 compliant environments, ensuring model auditability.
• Standards: Knowledge of CDISC (SDTM/ADaM) data structures is a significant plus.

Skills: ai/ml,python,data science