Data Scientist – Time Series, Statistical Modelling & Azure Data Bricks

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the  British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery. 

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

We believe that diversity of people and perspective gives us a competitive advantage.

We are seeking a Data Scientist with strong expertise in Python, Statistical Modelling, Forecasting, and Machine Learning to develop advanced analytics solutions using Azure and Databricks. The ideal candidate will have hands-on experience in time-series forecasting, geospatial analytics, model interpretation, and large-scale data analysis to support data-driven decision making.

• →Perform advanced data analysis, feature engineering, and exploratory analytics using Python.
• →Develop, validate, and deploy predictive and machine learning models.
• →Design and implement statistical forecasting and time-series models.
• →Build geospatial analytics and location-based modelling solutions.
• →Apply model explainability techniques and communicate insights to business stakeholders.
• →Develop scalable analytical solutions using Databricks and Azure.
• →Collaborate with business and technical teams to translate requirements into analytical solutions.
• →Ensure data quality, governance, and validation throughout the model lifecycle.
• →Contribute to reusable analytical frameworks, standards, and best practices.

• Statistical Modelling and Forecasting
• Time-Series Analysis
• Machine Learning
• Model Explainability & Interpretation
• Feature Engineering
• Hypothesis Testing and Predictive Analytics

• Python
• SQL

• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• Prophet
• Scipy

• Experience with geospatial data analysis and modelling
• GeoPandas, Shapely or similar libraries (preferred)

• Databricks
• Azure Data & Analytics ecosystem
• Spark / PySpark

• Energy / Utilities domain experience
• Forecasting, optimization, or operational analytics use cases
• Experience communicating analytical insights to business stakeholders

• Azure ML / MLOps
• SHAP, LIME, or similar Explainable AI frameworks