{"id":3108671,"url":"https://alion.io/job/exl-senior-data-scientist-4","title":"Senior Data Scientist","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":80,"open_postings":269,"ghost_share":0,"stale_share":0.996,"repost_share":0.004,"time_to_fill_p50_days":15,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":62000,"max_usd":110000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":90},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Feature Store","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"XGBoost","optional":false},{"name":"CI/CD","optional":true},{"name":"Explainable AI","optional":true}],"status":"live","first_seen_at":"2026-09-14T13:43:19Z","employer_posted_date":"2026-09-14","last_verified_at":"2026-10-29T14:13:53Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":45},"description":"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.\nThis 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.\n Technical & Solution Leadership\nDesign, develop, and review machine learning solutions across insurance domains, including claims, underwriting, sales and marketing. \nTake ownership of:Feature engineering using large-scale insurance datasets\nModel selection, training, validation, and performance tuning\nHandling highly imbalanced datasets, weak labels, and proxy targets\nTranslating business rules into ML features / hybrid rule-ML systems\n\nEnsure model explainability, stability, and governance aligned with insurance and regulatory expectations (e.g., interpretable ML, bias mitigation).\n2. Stakeholder & Program Collaboration\nAct as the client facing data scientist who manages client relationshipsPrepare demos and sprint review materials\n\nTranslate high-level business problems into:Well-defined analytics use cases\nModeling approaches and delivery plans\n\nParticipate in:Architecture and solution design discussions\nModel walkthroughs with technical and business stakeholders\nUAT discussions and model acceptance criteria definition\n\nCommunicate risks, dependencies, and delivery trade-offs early and clearly.\n3. Data, Platform & MLOps Alignment\nWork with data engineering and platform teams to:Shape analytical data models and feature stores\nEnsure production readiness of models\n\nContribute to:MLOps design (model versioning, monitoring, retraining strategies)\nDeployment patterns on modern analytics platforms (e.g., cloud-based data & ML stacks)\n\nEnsure models meet enterprise standards for scalability, reliability, and auditability.\n Experience & Domain\n5 - 8 years of experience in advanced analytics / data science\nInsurance domain experience (P&C, Life, Health, Group Benefits, or Claims) strongly preferred.\nProven experience delivering end-to-end ML solutions in production environments\nTechnical Skills\nStrong hands-on experience in:Python (pandas, scikit-learn, XGBoost / LightGBM, etc.)\nStatistical modeling and ML algorithms (classification, regression, segmentation)\n\nDeep understanding of:Feature engineering on transactional / behavioral data\nImbalanced classification techniques\nModel evaluation, stability, and drift monitoring\n\nExperience working with SQL and large-scale datasets.\nFamiliarity with modern ML platforms, cloud data environments, or analytics fabrics is a plus.\nStakeholder Management & Communication\nExperience working with offshore or distributed data science teams.\nStrong story telling skills to explain complex analytical concepts to:Non-technical stakeholders\nOnsite leadership and clients\n\nComfortable working across time zones and in a matrix delivery model.\nPreferred / Nice-to-Have\nExposure to:Model governance and regulatory expectations\nExplainable AI (XAI) techniques\nMLOps pipelines and CI/CD for 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