Data Scientist

Job Description

Senior Data Scientist – Fraud Analytics (LTC Claims)

Role Summary

We are seeking a Senior Data Scientist (6–7 years of experience) to support a fraud analytics initiative focused on Long‑Term Care (LTC) insurance claims. This is a client‑facing role requiring strong analytical expertise, hands‑on modeling experience, and the ability to independently drive analysis, present insights, and collaborate with stakeholders.

The ideal candidate will have a solid foundation in statistical modeling and hypothesis testing, combined with deep rooted experience in tree‑based and ensemble machine learning models, and cloud‑based data platforms.

 

Key Responsibilities

• Develop and deploy fraud detection models for LTC insurance claims using statistical and machine learning techniques
• Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing to identify fraud patterns and anomalies
• Build, evaluate, and optimize traditional statistical models as well as tree‑based models such as Random Forest, XGBoost, CatBoost, LightGBM etc.
• Independently conduct data analysis, research, and model experimentation, and translate findings into actionable insights
• Write clean, efficient, and production‑ready code using Python and SQL
• Work extensively with large datasets using cloud platforms, primarily Google Cloud Platform (GCP)
• Query and manage data using BigQuery, and handle datasets stored in Cloud Storage (Buckets)
• Use Git for version control, collaboration, and code review
• Prepare clear, concise, and impactful presentations for clients, explaining analytical findings to both technical and non‑technical stakeholders
• Collaborate with business, data engineering, and client teams to ensure models align with fraud investigation and business objectives

 

Required Skills & Experience

• 6–7 years of hands‑on experience in data science, analytics, or applied machine learning
  • Strong understanding of statistical modeling, probability concepts and hypothesis testing
• Proven experience with tree‑based and ensemble machine learning models (RF, XGBoost, CatBoost, LightGBM)
• Expert‑level SQL for data extraction, transformation, and analysis
• Strong Python skills for data analysis and modeling
• Experience using Git for source code management
• Solid exposure to cloud‑based analytics environments, preferably Google Cloud Platform (GCP), BigQuery and Cloud Storage
• Ability to work independently, manage deliverables, and drive tasks end‑to‑end
• Excellent verbal and written communication skills, essential for a client‑facing role

 

Candidate Profile

• Bachelor’s/Master's degree in economics, statistics, mathematics, computer science/engineering, operations research or related analytics areas
• Strong data analysis experience with complex, real‑world datasets
• Superior analytical thinking and problem‑solving skills
• Outstanding written and verbal communication skills, with confidence in client interactions

 

Key Responsibilities

• Develop and deploy fraud detection models for LTC insurance claims using statistical and machine learning techniques
• Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing to identify fraud patterns and anomalies
• Build, evaluate, and optimize traditional statistical models as well as tree‑based models such as Random Forest, XGBoost, CatBoost, LightGBM etc.
• Independently conduct data analysis, research, and model experimentation, and translate findings into actionable insights
• Write clean, efficient, and production‑ready code using Python and SQL
• Work extensively with large datasets using cloud platforms, primarily Google Cloud Platform (GCP)
• Query and manage data using BigQuery, and handle datasets stored in Cloud Storage (Buckets)
• Use Git for version control, collaboration, and code review
• Prepare clear, concise, and impactful presentations for clients, explaining analytical findings to both technical and non‑technical stakeholders
• Collaborate with business, data engineering, and client teams to ensure models align with fraud investigation and business objectives

Required Skills & Experience

• 6–7 years of hands‑on experience in data science, analytics, or applied machine learning
  • Strong understanding of statistical modeling, probability concepts and hypothesis testing
• Proven experience with tree‑based and ensemble machine learning models (RF, XGBoost, CatBoost, LightGBM)
• Expert‑level SQL for data extraction, transformation, and analysis
• Strong Python skills for data analysis and modeling
• Experience using Git for source code management
• Solid exposure to cloud‑based analytics environments, preferably Google Cloud Platform (GCP), BigQuery and Cloud Storage
• Ability to work independently, manage deliverables, and drive tasks end‑to‑end
• Excellent verbal and written communication skills, essential for a client‑facing role

 

Candidate Profile

• Bachelor’s/Master's degree in economics, statistics, mathematics, computer science/engineering, operations research or related analytics areas
• Strong data analysis experience with complex, real‑world datasets
• Superior analytical thinking and problem‑solving skills
• Outstanding written and verbal communication skills, with confidence in client interactions