Senior Analyst - Data Science

LatentView Analytics Ltd.; Bengaluru; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

LatentView Analytics Ltd. is seeking a Senior Analyst - Data Science to design and validate ML models for healthcare AI and clinical intelligence.

The role involves feature engineering on longitudinal EHR data, building pipelines for unstructured data, and collaborating with clinicians to ensure real-world impact. The candidate will scope problems, define targets, and work with engineering teams to deploy and monitor models in production, while evaluating calibration, bias, and robustness across

Qualifications

• 3+ years of data science/ML experience.
• Strong Python and data processing skills.
• Experience with healthcare data preferred.

Responsibilities

• Model Development & Prototyping: Design, train, and validate ML models for risk stratification and healthcare insights.
• Longitudinal EHR Feature Engineering: Translate temporal clinical data into robust features.
• Unstructured Data Pipeline Design: Build pipelines to extract insights from unstructured records.
• Problem Scoping & Hypothesis Testing: Define ML targets and success metrics.
• Production Integration & Monitoring: Deploy prototypes and track performance.
• Comprehensive Model Evaluation: Assess offline metrics and real-world validity.
• Cross-Functional Collaboration: Partner with clinicians and technologists.

Skills

Machine Learning & Statistical Mod?

Tools

Pandas

NumPy

Scikit-learn

PyTorch

Job description

Designation : Senior Analyst - Data Science

Level : L2

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
• Production ML Deployment & Performance Monitoring
• Technical Communication & Cross-Functional Stakeholder Alignment