EXL 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 worlds leading corporations in industries including insurance, healthcare, banking and financial services, media, and retail, among others. Focused on 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 34,000 strong, with more than 50 offices spanning six continents.

EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXLs Human Resources team, as well as our hiring managers.

Role Overview

We are seeking a high-impact analytics professional with strong grounding in machine learning, statistical modeling, and applied analytics. The role will focus on building production-ready predictive models using Python and SAS/R, primarily for Property & Casualty (P&C) insurance use cases. Candidates from marketing analytics with strong modeling depth are also suitable.

This is a hands-on role, not a reporting or BI position.

Key Responsibilities

Core Analytics & Modeling

- Design, develop, and deploy machine learning and statistical models (GLM, tree-based models, ensemble methods, regression, clustering).

- Build end-to-end analytics pipelines: data extraction, feature engineering, model training, validation, and monitoring.

- Apply advanced analytical techniques to solve business problems with measurable impact.

Insurance / Domain Use Cases (Preferred)

- Pricing, underwriting, claims, loss prediction, fraud detection, retention, and risk segmentation.

- Work closely with business stakeholders to translate insurance problems into analytical solutions.

Marketing Analytics (Alternative Domain)

- Propensity modeling, churn prediction, campaign optimization, customer lifetime value, and response modeling.

- Support experimentation frameworks (A/B testing, uplift modeling).

Technology & Tools

- Develop models primarily in Python; maintain or translate models in SAS or R where required.

- Write efficient, production-grade code and ensure reproducibility.

- Collaborate with data engineering and IT teams for deployment and scalability.

Stakeholder & Delivery

- Communicate insights, assumptions, and model outcomes clearly to non-technical stakeholders.

- Ensure models meet governance, validation, and documentation standards.

- Mentor junior analysts if operating at lead level.

Required Skills & Qualifications

Must-Have

- Strong experience in Machine Learning / Advanced Analytics.

- Hands-on proficiency in Python.

- Working knowledge of SAS or R (model development or legacy model maintenance).

- Solid understanding of statistics, probability, and predictive modeling.

- Ability to work independently on ambiguous business problems.

Preferred

- Experience in Property & Casualty / General Insurance analytics.

- Exposure to pricing, underwriting, claims, or fraud analytics.

- Experience with model validation, monitoring, and governance.

- Familiarity with cloud environments or production deployment (nice to have, not mandatory).

Experience & Education

- 3-7 years of relevant analytics or data science experience.

- Bachelors or Masters degree in Statistics, Mathematics, Engineering, Economics, Data Science, or related fields.

What This Role Is NOT

- Not a pure BI, MIS, or dashboarding role.

- Not limited to SQL/reporting or descriptive analytics.