Lead Data Scientist – Risk & Customer Insights - Immediate Joiner

Hiring for: An exciting InsurTech startup building an AI-native stack for the Insurance.

Role: Data Scientist – Risk & Customer Insights

Positions: 1

Experience: 10 to 14 years

Location(s): Mumbai

Type: On-site / Permanent

Salary: 20 LPA to 40 LPA (Based on the fitment)

Notice Period: Immediate to 15 days

Key Responsibilities 

Data Science & Machine Learning 

• Lead the end-to-end lifecycle of machine learning and advanced analytics initiatives. 

• Design, develop, validate, and deploy predictive and prescriptive models for BFSI and insurance use 

cases. 

• Build scalable data science solutions leveraging structured and unstructured data. 

• Drive model monitoring, performance tracking, explainability, and governance practices. 

• Establish best practices for experimentation, feature engineering, model evaluation, and MLOps. 

Fraud & Risk Analytics 

• Design and enhance fraud detection and fraud prevention models. 

• Build risk scoring, propensity, anomaly detection, and behavioral analytics models. 

• Develop decisioning frameworks for underwriting, claims, collections, and customer risk assessment. 

• Partner with business stakeholders to translate risk and fraud strategies into analytical solutions. 

• Continuously improve model effectiveness while balancing customer experience and operational 

efficiency. 

AI & Agentic Decisioning 

• Explore and implement AI agent architectures that assist or automate business decision-making. 

• Build agentic workflows that combine LLMs, predictive models, rules engines, and enterprise data. 

• Develop AI-driven copilots and decision support systems for operational teams. 

• Evaluate emerging AI technologies and identify practical applications within insurance and BFSI 

workflows. 

• Drive responsible AI adoption with a focus on explainability, governance, and compliance. 

Leadership & Stakeholder Management 

• Lead and mentor a team of data scientists and machine learning engineers. 

• Work closely with product and engineering teams to operationalize analytical solutions. 

• Engage directly with customers and business leaders to understand challenges and define analytical 

roadmaps. 

• Contribute to Client's AI and data science strategy. 

• Drive a culture of innovation, experimentation, and continuous learning. 

Required Qualifications 

• 10–14 years of experience in Data Science, Machine Learning, Advanced Analytics, or related fields. 

• Strong experience within Banking, Financial Services, Insurance, FinTech, InsurTech, or related 

domains. 

• Proven experience building and deploying machine learning models in production environments. 

• Demonstrated experience in fraud analytics, fraud detection, risk modeling, underwriting analytics, 

claims analytics, or related domains. 

• Strong understanding of supervised and unsupervised learning techniques. 

• Hands-on expertise in Python and common data science libraries (Pandas, Scikit-learn, XGBoost, 

LightGBM, TensorFlow, PyTorch, etc.). 

• Experience working with SQL and large-scale data platforms. 

• Familiarity with cloud environments and modern MLOps practices. 

• Strong communication, customer-facing, and stakeholder management skills. 

Preferred Qualifications 

• Experience within the insurance industry. 

• Experience with GenAI, LLMs, RAG architectures, AI agents, and agentic workflows. 

• Familiarity with decision engines, rules engines, and enterprise workflow platforms. 

• Experience leading teams and mentoring data scientists. 

• Exposure to model governance, explainability, and regulatory requirements within BFSI. 

What Success Looks Like 

• Delivery of measurable business impact through fraud, risk, underwriting, and decisioning models. 

• Successful deployment of AI-powered decision support systems and agentic solutions. 

• Strong model governance, scalability, reliability, and production adoption. 

• High-quality collaboration across product, engineering, and business teams. 

• Development of a high-performing data science function capable of supporting rapid growth and 

innovation.