Senior Data Scientist - Machine Learning

As a Senior Data Scientist in Mumbai with 7-11 years of experience in Data Science and Machine Learning, your role will involve the following key responsibilities: - Design, develop, and deploy machine learning and AI solutions for Health Insurance use cases. - Build predictive models using techniques such as XGBoost, Artificial Neural Networks (ANN), and Deep Learning. - Work on business-critical use cases including claims prediction, fraud detection, underwriting, pricing, and risk modeling. - Improve model performance through feature engineering, hyperparameter tuning, and algorithm optimization. You will be responsible for managing the complete machine learning lifecycle, including data extraction, preprocessing, model development, validation, deployment, and monitoring. Collaborating with engineering teams to ensure production-grade deployment of AI/ML models will also be a crucial part of your role. Additionally, maintaining model documentation, version control, and reproducibility standards will be essential. Your role will also involve conducting A/B testing, statistical analysis, and post-deployment performance monitoring. Identifying model improvement opportunities and optimizing solutions based on business outcomes and feedback will be key aspects. Evaluating and adopting emerging AI/ML techniques and methodologies to drive innovation will also be part of your responsibilities. Collaborating with Health Insurance business stakeholders to understand requirements and translate them into scalable analytical solutions will be essential. Aligning AI/ML initiatives with key business objectives, including claims accuracy, fraud reduction, risk assessment, and operational efficiency, will be a significant part of your role. Presenting insights, recommendations, and model outcomes to both technical and non-technical stakeholders is also crucial. As a technical leader, you will establish and promote best practices for coding, model development, deployment, and documentation. Mentoring junior data scientists and contributing to capability building within the team will be expected. Staying current with emerging trends and advancements in AI, machine learning, and data science is imperative. You will be responsible for developing reusable machine learning pipelines and scalable frameworks for multiple health insurance use cases. Ensuring AI solutions are production-ready, maintainable, and scalable across business functions will be a key responsibility. Driving standardization, knowledge sharing, and continuous improvement across teams will also be part of your role. Qualifications Required: - 7-11 years of experience in Data Science, Machine Learning, or related fields. - Strong experience in the Health Insurance domain (Mandatory). - Expertise in machine learning algorithms such as XGBoost, Random Forest, Neural Networks, and Deep Learning models. - Strong understanding of claims analytics, underwriting, fraud detection, pricing, and risk modeling. - Hands-on experience with Python, SQL, and machine learning libraries such as Scikit-learn, TensorFlow, and/or PyTorch. - Experience with end-to-end model deployment and MLOps practices. - Experience working with large-scale structured and unstructured datasets. - Strong analytical, problem-solving, and statistical modeling skills. - Excellent communication and stakeholder management abilities. Please note that this job description is based on the information provided and does not include any additional details about the company. As a Senior Data Scientist in Mumbai with 7-11 years of experience in Data Science and Machine Learning, your role will involve the following key responsibilities: - Design, develop, and deploy machine learning and AI solutions for Health Insurance use cases. - Build predictive models using techniques such as XGBoost, Artificial Neural Networks (ANN), and Deep Learning. - Work on business-critical use cases including claims prediction, fraud detection, underwriting, pricing, and risk modeling. - Improve model performance through feature engineering, hyperparameter tuning, and algorithm optimization. You will be responsible for managing the complete machine learning lifecycle, including data extraction, preprocessing, model development, validation, deployment, and monitoring. Collaborating with engineering teams to ensure production-grade deployment of AI/ML models will also be a crucial part of your role. Additionally, maintaining model documentation, version control, and reproducibility standards will be essential. Your role will also involve conducting A/B testing, statistical analysis, and post-deployment performance monitoring. Identifying model improvement opportunities and optimizing solutions based on business outcomes and feedback will be key aspects. Evaluating and adopting emerging AI/ML techniques and methodologies to drive innovation will also be part of your responsibilities. Collaborating with Health Insura