Senior Data Scientist

About the Role

We are seeking a seasoned Senior Data Scientist/Analyst to join our dynamic Data Science & Analytics team in Mumbai. The role requires a strong understanding of sales processes, advanced analytical expertise, working knowledge of data science, and the ability to convert complex data into actionable insights that drive revenue growth, market-share improvement, channel performance, and customer outcomes.
The role will manage a team of data analysts and data scientists, collaborate closely with Sales, Marketing, Product, Technology, Finance, and Compliance teams, and build scalable analytical frameworks, dashboards, and predictive models to support sales strategy and execution.

Key Responsibilities

• End-to-End Project Leadership: Take ownership of data science/analytics projects, from initial research and experimentation to scalable deployment, monitoring, and ongoing optimization. Work closely with vertical leadership to understand business priorities and translate them into analytical use cases and delivery roadmaps.
• Model Development: Design, build, and optimize advanced statistical and machine learning models to solve complex business problems. Build or guide predictive models for lead scoring, conversion propensity, cross-sell, up-sell, customer segmentation, channel performance, demand forecasting, and sales prioritization.
• IRDAI Data Visibility and Market-Share Analytics: Possess hands-on experience in working with IRDAI-published data and other relevant insurance-industry datasets for market-share and competitive analytics. • Track and analyse market share across products, business segments, channels, geographies, and relevant time periods using available IRDAI data. Interpret regulatory and industry data to identify market trends, competitive movements, emerging opportunities, and potential business risks.
• LLM & Generative AI: Research and implement innovative LLM/GenAI solutions, including advanced prompt engineering, Retrieval-Augmented Generation (RAG) frameworks, and parameter-efficient fine-tuning for specific business needs.
• Cross-functional Collaboration: Partner with product, engineering, and business stakeholders to translate complex challenges into actionable data science solutions and communicate findings effectively to technical and non-technical audiences.
• Mentorship: Guide and mentor junior data scientists and analysts, fostering a culture of technical excellence and continuous learning.
• Infrastructure & Deployment: Work with data engineering teams to design scalable data models and robust, automated ML pipelines using MLOps best practices and cloud services (AWS/GCP/Azure).
• Performance Monitoring: Implement monitoring frameworks to track and enhance the performance of deployed models.
  
  

Core Technical Skills

Machine Learning & Statistics:
o Strong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering.
o Practical experience with hands-on modelling and experimentation.
Programming:
o Proficiency in Python, R, and SQL for model development and data analytics.
Visualization
o Strong experience with Power BI, Tableau, or comparable business-intelligence and visualisation platforms.
LLM & Generative AI:
o Conceptual and practical understanding of LLM architecture, prompt engineering, RAG, parameter fine-tuning, and LLM evaluation/monitoring.
o Exposure to vector databases
o Executed at least 1 impactful LLM/GenAI implementation in real-world settings.
Deployment & Automation:
o Experience with cloud platforms (AWS/GCP/Azure)
o API integration and orchestration for scalable solutions.
o Practical experience with MLOps tools like MLflow, Docker, or Kubernetes for CI/CD pipelines.

Soft Skills

Strong analytical thinking and structured problem-solving ability.
Excellent interpersonal communication and stakeholder engagement.
Ability to work in a fast-paced, collaborative environment and lead through influence.
High ownership and drive to deliver measurable impact.
Collaborate with product, engineering, and business stakeholders to translate complex AI solutions into business impact.

Educational Background

Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, Mathematics – Preferably from
Equivalent industry experience with strong hands-on contributions will be preferred.