Sr. Data Scientist

About the job

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

Requirements

• ai/ml
• python
• data science

Preferred Technologies

• ai/ml
• python
• data science