Sr. Data Scientist

Sr Data Scientist- Insights & Analytics

Key Accountabilities

1. Develop innovative approaches to assist business partners in achieving objectives through analysis andmodelling.

1. ApplyML and AIto analytics algorithms to build inferential and predictive models allowing for scalable solutions to be deployed across the business

1. Developend-to-endbusiness solutions from data extraction, data preparation, data mining toMLmodeling and thenpresent insights in an easy to interpret way to the business teams

1. Effectively communicate the why and how of data-driven recommendations to cross-functional teams, employing storytelling techniques.

1. Collaborate as a member of the team, actively engaging with and seeking feedback from other Data Scientists.

1. Mentors junior data scientist in team

1. Identifyand develop long-term data science processes, frameworks, tools, and standards.

1. Proactivelyidentifiesand addresses issues/challengesby developing effective innovative solutions independently

1. Contributestoidentifyingopportunities forleveragingvariousdatasetsto drive business solutions

1. Proactively shares knowledge andexpertise

1. Managestakeholderrelationships independently to drive projects

1. Be able to explore and troubleshoot niche technologies and provide automation solutions.

Minimum Qualifications

1. Minimum of 8-10 years ofrelevantexperience with a bachelor'sdegreeor 6-8 years with amaster'sdegreein engineering, Data Science, Applied Mathematics, Statistics,economicsor related field.

1. Deepexpertisein advanced machine learning, statistical modeling, and time-series analysis, with the ability to select and tailor methods based on business context and data characteristics.

1. Proventrack recordof designing, building, andoptimizinglarge-scale forecasting systems that drive measurable business impact across categories, markets, or regions.

1. Extensive hands-on experience with time-series forecasting techniques, including but not limited to Prophet, ARIMA/SARIMA, Exponential Smoothing, and hierarchical forecasting approaches.

1. Strong command of regression modeling, econometrics, and causal inference, withdemonstratedability to apply these methods toidentify, quantify, and explain business drivers in real-world settings.

1. Advanced experience with supervised and unsupervised learning, including regression, classification, clustering, decision trees, ensemble models, and feature engineering for structured business data.

1. Expert-levelproficiencyin Python and/or R and SQL, with a strong focus on writing production-quality, scalable, and well-documented code.

1. Hands-on experience deploying analytics solutions on cloud platforms and big data ecosystems, enabling scalable, automated, and reproducible forecasting pipelines.

1. Strong ability to translate complex analytical outputs into clear, actionable business insights, influencing decision-making at senior stakeholderlevelsand directly supporting businessobjectives.

1. Demonstrated ownership of end-to-end analytics projects, from problem framing and data exploration to model development, validation, deployment, and performance monitoring.

1. Experience building and operationalizing machine learning applications, tools, or decision-support systems, with an emphasis on robustness, interpretability, and long-term business value.

1. Ability to work independently and deal with ambiguity.

1. Wholesome thinking -not just ML models but also business strategies and analytical products.

1. Working knowledge of Agentic AI systems, including how they are designed and implemented, along with hands-on exposure toimplementation and usage ofLarge Language Models (LLMs)/AI tools, is a plus

Preferred Qualifications/ Skills/ Job Experiences