AVP/DVP Data Science AI, ML - Life Insurance - Goregaon

Job Description

AVP/DVP Data Science AI, ML - Life Insurance - Goregaon AI & Machine Learning Design, develop, and implement machine learning models for predictive analytics, natural language processing (NLP), computer vision, or recommendation systems. Lead end-to-end AI project lifecycles, including validation, productionization, and optimization using frameworks such as TensorFlow, PyTorch, and scikit-learn. Stay updated with the latest advancements in Generative AI and foundation models, evaluating and integrating them as needed. Partner with cross-functional teams to identify AI use cases and deliver measurable business value. Data Science & Analytics Lead the end-to-end analytics lifecycle, from data exploration to actionable insights and product development. Collaborate with data engineers and analysts to build impactful solutions that drive business outcomes. Leadership & Strategy • Mentor and guide junior team members, fostering their professional growth. Cloud Architecture & Engineering Contribute to the enterprise AI and cloud strategy, including roadmap planning and vision setting. Architect and design cloud-native AI and data platforms on GCP (preferred), AWS, or Azure. Optimize data pipelines and machine learning workflows using services like Vertex AI, SageMaker, or Azure ML. Ensure scalable, secure, and cost-efficient architectures with robust CI/CD pipelines in cloud environments. Experience • 8+ years of experience in AI and Data Science roles. Required Skills Proficiency in Python and SQL. Strong expertise in machine learning frameworks like TensorFlow, PyTorch, Hugging Face, and scikit learn Hands-on expertise with large language models (LLMs), vector databases, and retrieval-augmented generation (RAG) techniques In-depth knowledge of cloud platforms, preferably GCP, but also AWS or Azure. Familiarity with data engineering tools such as BigQuery, Kafka, Snowflake. Sound knowledge of data privacy, governance, and ethical AI practices. Expertise in MLOps practices, including CI/CD pipelines, model monitoring, and A/B testing.