Job Description

EXL is seeking an experienced and driven Data Scientist to support advanced analytics initiatives for our global clients. This role focuses on developing predictive models, extracting insights from large-scale datasets, and translating analytical findings into actionable business strategies. The ideal candidate brings deep technical expertise, hands-on modelling experience, and a strong ability to communicate results to both technical and business stakeholders.

• Develop and deploy advanced statistical and machine learning models to solve diverse business problems across client engagements.
• Translate complex business challenges into analytical frameworks and deliver high-impact insights that influence strategy and operations.
• Analyze large, complex datasets using Python, SQL, PySpark, and statistical tools to surface meaningful patterns and trends.
• Collaborate with stakeholders across marketing, product, and data teams to understand needs and deliver actionable, data-driven solutions.
• Build dashboards and visualizations that communicate insights clearly to cross-functional teams and senior leadership.
• Stay current with the latest analytics trends and emerging techniques, continuously elevating the team's analytical approach.
• Contribute to and help establish best practices in model development, validation, and deployment.

What We’re Looking For

• Bachelor's or Master's degree in Statistics, Computer Science, Economics, or a related quantitative field.
• 3–8 years of experience in data science or advanced analytics, with at least 2–3 years in the media or entertainment domain.
• Proficiency in Python, SQL, and ML/statistical libraries such as scikit-learn, XGBoost, and statsmodels.
• Strong storytelling and communication skills, with a track record of presenting analytical findings to business stakeholders.
• Ability to independently manage analytics projects end-to-end while collaborating effectively across technical and business teams.

Nice to Have

• Experience deploying models on cloud platforms such as AWS, GCP, or Azure.
• Exposure to Generative AI applications and LLM-assisted analytics workflows.
• Proven ability to work with global stakeholders in cross-functional, matrixed environments.
• Familiarity with productionizing models and partnering closely with MLOps or engineering teams.