In NEO we are looking for a "Senior Data Scientist" with the below key responsibilities

1. Advanced Analytics & Predictive Modeling

• Translate complex business problems into analytical and predictive modeling solutions.
• Develop, evaluate, and optimize machine learning and statistical models.
• Apply advanced analytics to understand business drivers, trends, patterns, and relationships.
• Develop predictive models for claims, customer behavior, portfolio performance, and business outcomes.

2. Risk & Insurance Analytics

• Develop data-driven approaches for risk assessment, segmentation, and risk scoring.
• Model claim frequency, claim severity, expected losses, and loss ratios.
• Develop early-warning indicators and predictive risk monitoring capabilities.
• Support portfolio analysis, risk management, and data-driven underwriting decisions.

3. Forecasting & Scenario Analysis

• Develop forecasting models for claims, loss ratios, financial performance, customer behavior, and other business metrics.
• Perform trend analysis and scenario modeling to support planning and decision-making.
• Translate model outputs into actionable business recommendations.

4. AI & Generative AI

• Develop analytical solutions leveraging Generative AI, Large Language Models (LLMs), AI Agents, RAG, and embeddings.
• Identify opportunities to augment traditional analytics with AI-powered investigation, insight generation, and decision support.

5. Experimentation & Analytical Problem Solving

• Design analytical frameworks and experiments to evaluate business hypotheses.
• Apply statistical reasoning and causal analysis where appropriate.
• Identify root causes behind business performance and translate findings into practical recommendations.

6. Model Production & MLOps

• Collaborate with Engineers to productionize analytical and predictive models.
• Establish appropriate approaches for model monitoring, validation, versioning, retraining, and MLOps.
• Ensure models remain reliable, explainable, maintainable, and aligned with business objectives.

7. Business Partnership & Communication

• Work closely with Product, Underwriting, Claims, Finance, Operations, Technology, and leadership teams.
• Communicate analytical findings clearly to both technical and non-technical audiences.
• Turn complex analytical outputs into clear insights, recommendations, and decisions.

Required Qualifications

• 4+ years of experience in Data Science, Machine Learning, Advanced Analytics, or a related field.
• Strong foundation in statistics, probability, machine learning, and predictive modeling.
• Strong programming skills in Python and SQL.
• Strong experience in feature engineering, model evaluation, validation, and experimentation.
• Practical experience with forecasting and time-series modeling.
• Experience solving real-world business problems using data.
• Strong analytical and problem-solving capabilities.
• Ability to communicate technical concepts and analytical findings effectively.

Insurance experience is valuable, but strong experience solving complex, high-volume business problems through data and analytics is equally relevant.