Company Overview and Culture

EXL (NASDAQ: EXLS) is a global analytics and digital solutions company that partners with clients to improve business outcomes and unlock growth. Bringing together deep domain expertise with robust data, powerful analytics, cloud, and AI, we create agile, scalable solutions and execute complex operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media, and retail, among others. Focused on creating value from data for driving faster decision-making and transforming operating models, EXL was founded on the core values of innovation, collaboration, excellence, integrity and respect. Headquartered in New York, our team is over 40,000 strong, with more than 50 offices spanning six continents. For information, visit www.exlservice.com.

For the past 20 years, EXL has worked as a strategic partner and won awards in its approach to helping its clients solve business challenges such as digital transformation, improving customer experience, streamlining business operations, taking products to market faster, improving corporate finance, building models to become compliant more quickly with new regulations, turning volumes of data into business opportunities, creating new channels for growth and better adapting to change. The business operates within four business units: Insurance, Health, Analytics, and Emerging businesses.

Job 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. 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

What we offer

EXL Analytics offers an exciting, rapid paced and innovative environment, which brings together a group of sharp and entrepreneurial professionals who are eager to influence business decisions. From your very first day, you get an opportunity to work closely with highly experienced, world class analytics consultants.

You can expect to learn many aspects of businesses that our clients engage in. You will also learn effective teamwork and time-management skills - key aspects for personal and professional growth

Analytics requires different skill sets at different levels within the organization. At EXL Analytics, we invest heavily in training you in all aspects of analytics as well as in leading analytical tools and techniques.

We provide guidance/ coaching to every employee through our mentoring program wherein every junior level employee is assigned a senior level professional as advisors

The experience at EXL Analytics sets the stage for further growth and development in our company and beyond

📌 Senior Data Scientist - toronto
🏢 exl
📍 Toronto