Data science intern analyzing HR data and developing predictive models and AI solutions. Supporting iA Financial Group’s insurance, savings, and wealth management business.
About the role
- Senior Data Scientist delivering advanced analytics and machine-learning solutions for insurance transformation. Leading model development, stakeholder collaboration, and production-ready MLOps delivery.
Responsibilities
- Design, develop, and review machine learning solutions across insurance domains, including claims, underwriting, sales, and marketing
- Own feature engineering using large-scale insurance datasets
- Lead model selection, training, validation, and performance tuning
- Handle highly imbalanced datasets, weak labels, and proxy targets
- Translate business rules into ML features and hybrid rule-ML systems
- Ensure model explainability, stability, and governance aligned with insurance and regulatory expectations
- Act as the client-facing data scientist and manage client relationships
- Prepare demos and sprint review materials
- Translate high-level business problems into defined analytics use cases, modeling approaches, and delivery plans
- Participate in architecture and solution design discussions, model walkthroughs, UAT discussions, and model acceptance criteria definition
- Communicate risks, dependencies, and delivery trade-offs
- Coordinate with offshore teams to ensure delivery quality
- Work with data engineering and platform teams to shape analytical data models and feature stores
- Ensure production readiness of models
- Contribute to MLOps design, including model versioning, monitoring, and retraining strategies
- Contribute to deployment patterns on modern analytics platforms and ensure enterprise standards for scalability, reliability, and auditability
Requirements
- 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
- Strong hands-on experience with Python, including pandas, scikit-learn, XGBoost / LightGBM
- Statistical modeling and ML algorithms, including classification, regression, and segmentation
- Deep understanding of feature engineering on transactional / behavioral data
- Deep understanding of imbalanced classification techniques
- Deep understanding of model evaluation, stability, and drift monitoring
- Experience working with SQL and large-scale datasets
- Experience working with offshore or distributed data science teams
- Strong storytelling 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
- Familiarity with modern ML platforms, cloud data environments, or analytics fabrics is a plus
- Exposure to model governance and regulatory expectations, Explainable AI (XAI) techniques, and MLOps pipelines and CI/CD for analytics is preferred / nice-to-have
Benefits
- Work From Home / remote work
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