Data Science Consultant

Job Summary

The Data Scientist / Analyst Consultant will design, develop and deploy advanced analytics and AI/ML models to detect fraud, waste and abuse across the AB PM-JAY ecosystem. The role involves working with large volumes of claims, beneficiary, provider and transaction data, engineering fraud features, building and validating detection triggers, and converting model outputs into investigation-ready suspicious claims and cards. The Data Scientist will build triggers using advanced analytics techniques including NLP, OCR, Social Network Analysis and deep learning, support their validation with the National Anti-Fraud Unit (NAFU), and continuously improve model efficacy based on feedback and confirmed fraud outcomes.

Key Responsibilities

• Design, develop and deploy statistical and machine learning models for fraud, waste and abuse detection on scheme claims and card data.

• Perform advanced data analytics including clustering, classification, profiling, anomaly detection, predictive modelling, scoring, regression, link prediction, co-occurrence grouping, similarity matching and causal modelling.

• Develop fraud detection triggers using advanced analytics techniques such as NLP, OCR, Social Network Analysis and deep learning, and document trigger construct, logic and thresholds for review and approval.

• Apply both supervised and unsupervised learning approaches, training models on labelled historical fraud data and scheme ecosystem data.

• Conduct data profiling, quality assessment, cleaning, standardisation and feature engineering to create analysis-ready datasets.

• Execute triggers and models on claim and card data within defined turnaround timelines and generate case-level suspicious output with trigger reasons and evidence fields.

• Validate model outputs, assess false positive risk, calibrate thresholds and measure trigger efficacy against defined performance benchmarks.
• Fine-tune and retrain models on the basis of subsequent events, reviewer feedback and confirmed fraud outcomes.

• Collaborate with medical experts, business analysts, subject matter experts and technology teams to translate domain knowledge and clinical rules into executable analytical logic.

• Prepare model documentation, trigger submission packs, dashboards and reports for internal review committees and client-facing evaluations.

Preferred Skills

• Experience in healthcare analytics, health insurance claims analytics or fraud, waste and abuse detection.

• Experience in fraud analytics roles with a proven track record of developing and implementing fraud detection models and strategies.

• Knowledge of AB-PMJAY / health benefit packages / claims adjudication workflows.

• Exposure to Social Network Analysis, graph analytics or link analysis for collusion and network detection.

• Familiarity with OCR, image analytics and document forgery or duplication detection.

• Experience with model deployment, versioning and MLOps practices.

• Exposure to dashboarding and BI tools such as Power BI, Tableau or equivalent.

Experience

• Minimum 5 years of experience in analytics and managing big data, out of which at least 3 years in developing AI/ML models.

• Demonstrable credentials of having worked on and developed AI/ML models independently.

• Experience in fraud analytics, anomaly detection or risk modelling will be preferred.

• Experience in healthcare, health insurance or government health scheme data will be an added advantage.