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Senior Data Scientist

exl Service.com (India) Private Limited
CAN
Senior
Onsite
Discovered 1 months ago

Full Job Posting

Qualifications

  • Experience & Domain
  • 5 – 8 years of experience in advanced analytics / data science
  • Proven experience delivering end-to-end ML solutions in production environments

Technical Skills

  • Python (pandas, scikit-learn, XGBoost / LightGBM, etc.)
  • Statistical modeling and ML algorithms (classification, regression, segmentation)
  • Feature engineering on transactional / behavioral data
  • Imbalanced classification techniques
  • Model evaluation, stability, and drift monitoring
  • Experience working with SQL and large-scale datasets.
  • Stakeholder Management & Communication
  • Experience working with offshore or distributed data science teams.
  • Non-technical stakeholders
  • Onsite leadership and clients
  • Comfortable working across time zones and in a matrix delivery model.
  • Model governance and regulatory expectations
  • Explainable AI (XAI) techniques
  • MLOps pipelines and CI/CD for analytics

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).
  • 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.
  • 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.

Preferred Qualifications

  • Insurance domain experience (P&C, Life, Health, Group Benefits, or Claims) strongly preferred.
  • Familiarity with modern ML platforms, cloud data environments, or analytics fabrics is a plus.
  • Preferred / Nice-to-Have

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