Data Scientist- Insurance - PAN India (Delhi)

As a Senior Data Scientist in the insurance domain with 5-10 years of experience, your role will involve leveraging your expertise in machine learning, statistical modeling, and insurance analytics to develop fraud detection and risk analytics models for insurance claims. You will be expected to work on large insurance claims datasets and translate analytical insights into actionable business recommendations for stakeholders. Key Responsibilities: - Develop and deploy fraud detection and risk analytics models for insurance claims using statistical and machine learning techniques. - Conduct exploratory data analysis (EDA), feature engineering, and hypothesis testing to identify fraudulent claim patterns and anomalies. - Design and implement machine learning models for insurance analytics. - Analyze large insurance claims datasets to detect abnormal patterns, suspicious transactions, and fraud indicators. - Write efficient and scalable code using Python and SQL for data analysis, modeling, and automation. - Work with large scale datasets on cloud platforms, preferably Google Cloud Platform (GCP). - Query and analyze data using BigQuery, and manage datasets stored in Cloud Storage. - Maintain version control and collaborative workflows using Git. - Present analytical insights, model results, and fraud patterns to business stakeholders and clients through clear reports and presentations. - Collaborate with data engineering, business teams, and fraud investigation units to align models with real-world insurance processes. - Continuously improve model performance through experimentation, validation, and optimization techniques. Qualifications Required: - 6-8 years of experience in Data Science, Machine Learning, or Advanced Analytics. - Experience in Insurance domain, specifically in Claims Analytics, Fraud Detection, or Risk Analytics. - Strong understanding of statistics, probability, and hypothesis testing. - Proficiency in advanced SQL skills for large-scale data analysis. - Strong programming skills in Python for machine learning and data analysis. - Experience with Git for version control. - Exposure to cloud analytics environments, preferably Google Cloud Platform (GCP), BigQuery, and Cloud Storage. - Ability to work independently and manage end-to-end data science projects. - Excellent communication and stakeholder management skills. Additional Details: This role will involve working with insurance claims datasets such as LTC, Health, Life, and Property & Casualty. You will also have the opportunity to present data insights to clients and business leaders. Education: Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science/Engineering, Operations Research, Data Science, or related analytical field. As a Senior Data Scientist in the insurance domain with 5-10 years of experience, your role will involve leveraging your expertise in machine learning, statistical modeling, and insurance analytics to develop fraud detection and risk analytics models for insurance claims. You will be expected to work on large insurance claims datasets and translate analytical insights into actionable business recommendations for stakeholders. Key Responsibilities: - Develop and deploy fraud detection and risk analytics models for insurance claims using statistical and machine learning techniques. - Conduct exploratory data analysis (EDA), feature engineering, and hypothesis testing to identify fraudulent claim patterns and anomalies. - Design and implement machine learning models for insurance analytics. - Analyze large insurance claims datasets to detect abnormal patterns, suspicious transactions, and fraud indicators. - Write efficient and scalable code using Python and SQL for data analysis, modeling, and automation. - Work with large scale datasets on cloud platforms, preferably Google Cloud Platform (GCP). - Query and analyze data using BigQuery, and manage datasets stored in Cloud Storage. - Maintain version control and collaborative workflows using Git. - Present analytical insights, model results, and fraud patterns to business stakeholders and clients through clear reports and presentations. - Collaborate with data engineering, business teams, and fraud investigation units to align models with real-world insurance processes. - Continuously improve model performance through experimentation, validation, and optimization techniques. Qualifications Required: - 6-8 years of experience in Data Science, Machine Learning, or Advanced Analytics. - Experience in Insurance domain, specifically in Claims Analytics, Fraud Detection, or Risk Analytics. - Strong understanding of statistics, probability, and hypothesis testing. - Proficiency in advanced SQL skills for large-scale data analysis. - Strong programming skills in Python for machine learning and data analysis. - Experience with Git for version control. - Exposure to cloud analytics environments, preferably Google Cloud Platform (GCP), BigQ

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