Senior Analyst - Data Science
Posted Updated
Description
Designation : Senior Analyst - Data Science
Level : L2
Location : Bengaluru
Experience : 3+ years
Job Role : Data Scientist – Healthcare AI & Clinical Intelligence
Responsibilities :
- Model Development & Prototyping: Design, train, and validate machine learning and statistical models focused on risk stratification, disease progression, treatment intelligence, and healthcare recommendations.
- Longitudinal EHR Feature Engineering: Translate temporal clinical touchpoints—such as diagnostic records, symptoms, lab investigations, and prescriptions—into robust features for predictive models and generative AI systems.
- Unstructured Data Pipeline Design: Build models and pipelines capable of extracting insights from unstructured healthcare records, doctor notes, and medical terminologies.
- Problem Scoping & Hypothesis Testing: Frame open-ended clinical questions into clear, measurable machine learning objectives; define appropriate targets, data requirements, and success metrics.
- Production Integration & Monitoring: Work alongside engineering teams to operationalize prototypes into production environments and establish continuous model performance tracking.
- Comprehensive Model Evaluation: Construct evaluation systems that assess offline metrics alongside real-world clinical validity, model calibration, algorithmic bias, and robustness.
- Cross-Functional Collaboration: Partner with domain experts (clinicians) and technical stakeholders to ensure solutions directly enhance healthcare delivery and operational outcomes.
Required skills :
Primary Skillset:
- Machine Learning & Statistical Modeling (Model Design, Evaluation, Calibration)
- Python & Core Data Science Stack (Pandas, NumPy, Scikit-learn, PyTorch or similar)
- Advanced SQL & Complex Querying
- Working with Large-Scale, Messy & Longitudinal Data
- Problem Formulation (Translating ambiguous domain/business problems into concrete ML setups)
Secondary Skillset:
- Healthcare/EHR/Claims Data Processing & Clinical Ontologies
- Clinical NLP & Medical Language Models (including LLMs/Generative AI)
- Advanced Analytics: Time-Series Analysis, Survival Analysis, & Causal Inference
- Big Data Frameworks (e.g., PySpark)
- Production ML Deployment & Performance Monitoring
- Technical Communication & Cross-Functional Stakeholder Alignment