Job Description

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

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