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
Skills Required
- 5-8 years of experience in advanced analytics / data science
- Proven experience delivering end-to-end ML solutions in production
- Insurance domain experience (P&C, Life, Health, Group Benefits, or Claims)
- Hands-on Python (pandas, scikit-learn, XGBoost or LightGBM)
- Experience with statistical modeling and ML algorithms (classification, regression, segmentation)
- Feature engineering on transactional / behavioral data
- Techniques for imbalanced classification, weak labels, and proxy targets
- Model evaluation, stability, drift monitoring and governance
- Experience working with SQL and large-scale datasets
- Experience collaborating with offshore or distributed data science teams and client-facing communication
- Familiarity with modern ML platforms, cloud data environments, MLOps, CI/CD and feature stores
- Exposure to model governance, regulatory expectations, and explainable AI techniques
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
