Leads end-to-end machine learning delivery for insurance transformation programs, including feature engineering, model development, validation, tuning, explainability, governance, and production readiness. Translates business objectives into analytics use cases and delivery plans, manages client relationships, coordinates distributed teams, and supports architecture, UAT, MLOps, monitoring, and deployment discussions. Requires strong Python, statistical modeling, SQL, large-scale data, stakeholder communication, and production ML experience.
We are seeking a senior data scientist to lead delivery of advanced analytics and machine learning solutions for a large scale transformation program within insurance practice.
This role is critical to bridging business objectives, data science execution, and delivery excellence. The individual will remain deeply involved in model development and technical design, while also coordinate with offshore team to ensure delivery quality.
Responsibilities- Technical & Solution Leadership
- Design, develop, and review machine learning solutions across insurance domains, including claims, underwriting, sales and marketing.
- Take ownership of:
- Feature engineering using large-scale insurance datasets
- Model selection, training, validation, and performance tuning
- Handling highly imbalanced datasets, weak labels, and proxy targets
- Translating business rules into ML features / hybrid rule-ML systems
- Ensure model explainability, stability, and governance aligned with insurance and regulatory expectations (e.g., interpretable ML, bias mitigation).
2. Stakeholder & Program Collaboration
- Act as the client facing data scientist who manages client relationships
- Prepare demos and sprint review materials
- Translate high-level business problems into:
- Well-defined analytics use cases
- Modeling approaches and delivery plans
- Participate in:
- Architecture and solution design discussions
- Model walkthroughs with technical and business stakeholders
- UAT discussions and model acceptance criteria definition
- Communicate risks, dependencies, and delivery trade-offs early and clearly.
3. Data, Platform & MLOps Alignment
- Work with data engineering and platform teams to:
- Shape analytical data models and feature stores
- Ensure production readiness of models
- Contribute to:
- MLOps design (model versioning, monitoring, retraining strategies)
- Deployment patterns on modern analytics platforms (e.g., cloud-based data & ML stacks)
- Ensure models meet enterprise standards for scalability, reliability, and auditability.
Experience & Domain
- 5 – 8 years of experience in advanced analytics / data science
- Insurance domain experience (P&C, Life, Health, Group Benefits, or Claims) strongly preferred.
- Proven experience delivering end-to-end ML solutions in production environments
Technical Skills
- Strong hands-on experience in:
- Python (pandas, scikit-learn, XGBoost / LightGBM, etc.)
- Statistical modeling and ML algorithms (classification, regression, segmentation)
- Deep understanding of:
- Feature engineering on transactional / behavioral data
- Imbalanced classification techniques
- Model evaluation, stability, and drift monitoring
- Experience working with SQL and large-scale datasets.
- Familiarity with modern ML platforms, cloud data environments, or analytics fabrics is a plus.
Stakeholder Management & Communication
- Experience working with offshore or distributed data science teams.
- Strong story telling skills to explain complex analytical concepts to:
- Non-technical stakeholders
- Onsite leadership and clients
- Comfortable working across time zones and in a matrix delivery model.
Preferred / Nice-to-Have
- Exposure to:
- Model governance and regulatory expectations
- Explainable AI (XAI) techniques
- MLOps pipelines and CI/CD for analytics
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