Head of Data Science

Role Overview: As a leader in the data science, ML engineering, and analytics field, you will be responsible for mentoring a team of 200-250+ members. Your role will involve defining and driving the enterprise AI/ML vision, roadmap, and best practices. You will play a crucial role in promoting AI adoption across various products, platforms, and business units. Collaborating with CXOs, product heads, and business stakeholders will be essential to ensure alignment with organizational goals. Key Responsibilities: - Lead and mentor a large team of data science, ML engineering, and analytics professionals - Define enterprise AI/ML vision, roadmap, and best practices - Drive AI adoption across products, platforms, and business units - Collaborate with CXOs, product heads, and business stakeholders - Stay hands-on with model design, feature engineering, evaluation, and deployment - Build and review solutions involving predictive & prescriptive analytics, NLP, Computer Vision, Deep Learning, and MLOps - Ensure models are production-ready, scalable, and explainable - Architect and implement AI/ML pipelines on Databricks (Spark, MLflow, Delta Lake) - Optimize large-scale data processing and model training - Collaborate with cloud and data engineering teams - Work closely with middleware teams to integrate ML services via APIs/microservices - Enable real-time & batch model consumption - Ensure secure, scalable communication between applications, data platforms, and AI services - Apply AI to use cases in healthcare/insurance such as claims processing & fraud detection, risk scoring & underwriting, patient/member analytics, and operational optimization & compliance - Embed AI into enterprise software development lifecycle (SDLC) - Promote Agile, CI/CD, DevOps, and MLOps practices - Ensure high code quality, documentation, and governance Qualification Required: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field - Proven experience in leading and mentoring large data science, ML engineering, and analytics teams - Strong hands-on experience in model design, feature engineering, evaluation, and deployment - Proficiency in building solutions involving predictive & prescriptive analytics, NLP, Computer Vision, Deep Learning, and MLOps - Experience in architecting and implementing AI/ML pipelines on Databricks (Spark, MLflow, Delta Lake) - Familiarity with middleware tools such as Kafka, API Gateways, ESB, message queues, etc. - Knowledge of software development practices and promoting Agile, CI/CD, DevOps, and MLOps methodologies Additional Details: Omit this section as there are no additional company details mentioned in the provided job description. Role Overview: As a leader in the data science, ML engineering, and analytics field, you will be responsible for mentoring a team of 200-250+ members. Your role will involve defining and driving the enterprise AI/ML vision, roadmap, and best practices. You will play a crucial role in promoting AI adoption across various products, platforms, and business units. Collaborating with CXOs, product heads, and business stakeholders will be essential to ensure alignment with organizational goals. Key Responsibilities: - Lead and mentor a large team of data science, ML engineering, and analytics professionals - Define enterprise AI/ML vision, roadmap, and best practices - Drive AI adoption across products, platforms, and business units - Collaborate with CXOs, product heads, and business stakeholders - Stay hands-on with model design, feature engineering, evaluation, and deployment - Build and review solutions involving predictive & prescriptive analytics, NLP, Computer Vision, Deep Learning, and MLOps - Ensure models are production-ready, scalable, and explainable - Architect and implement AI/ML pipelines on Databricks (Spark, MLflow, Delta Lake) - Optimize large-scale data processing and model training - Collaborate with cloud and data engineering teams - Work closely with middleware teams to integrate ML services via APIs/microservices - Enable real-time & batch model consumption - Ensure secure, scalable communication between applications, data platforms, and AI services - Apply AI to use cases in healthcare/insurance such as claims processing & fraud detection, risk scoring & underwriting, patient/member analytics, and operational optimization & compliance - Embed AI into enterprise software development lifecycle (SDLC) - Promote Agile, CI/CD, DevOps, and MLOps practices - Ensure high code quality, documentation, and governance Qualification Required: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field - Proven experience in leading and mentoring large data science, ML engineering, and analytics teams - Strong hands-on experience in model design, feature engineering, evaluation, and deployment - Proficiency in building solutions involving predictive & prescriptive analytics, NLP, Computer V