Data Scientist Time Series, Statistical Modelling & Azure Data Bricks

Role Overview: As a Data Scientist specializing in Time Series, Statistical Modelling, and Azure Data Bricks at Capco, a global technology and management consulting firm, you will be responsible for developing advanced analytics solutions using Python, Statistical Modelling, Forecasting, and Machine Learning. Your expertise will be crucial in supporting data-driven decision-making by implementing time-series forecasting, geospatial analytics, model interpretation, and large-scale data analysis. Key Responsibilities: - 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. Qualification Required: - Strong background in Data Science & Analytics, including Statistical Modelling, Forecasting, Time-Series Analysis, Machine Learning, Model Explainability & Interpretation, Feature Engineering, Hypothesis Testing, and Predictive Analytics. - Proficiency in Programming with Python and SQL. - Familiarity with Libraries such as Pandas, NumPy, Scikit-learn, Statsmodels, Prophet, and Scipy. - Experience in Geospatial Analytics with knowledge of geospatial data analysis and modelling, preferably using GeoPandas, Shapely, or similar libraries. - Working knowledge of Cloud & Platform technologies including Databricks, Azure Data & Analytics ecosystem, Spark, and PySpark. - Preferred Experience in the Energy / Utilities domain, Forecasting, optimization, or operational analytics use cases, and communicating analytical insights to business stakeholders. - Good to Have skills in Azure ML / MLOps, and familiarity with frameworks like SHAP, LIME, or other Explainable AI frameworks. If you are passionate about leveraging your expertise in Statistical Forecasting, Time-Series Modelling, Geospatial Analytics, Model Explainability, Python, SQL, and Databricks, Capco offers an inclusive and diverse work culture that values your contributions, recognizes your potential, and provides opportunities for career growth. Visit www.capco.com for more information and connect with us on Twitter, Facebook, LinkedIn, and YouTube. Role Overview: As a Data Scientist specializing in Time Series, Statistical Modelling, and Azure Data Bricks at Capco, a global technology and management consulting firm, you will be responsible for developing advanced analytics solutions using Python, Statistical Modelling, Forecasting, and Machine Learning. Your expertise will be crucial in supporting data-driven decision-making by implementing time-series forecasting, geospatial analytics, model interpretation, and large-scale data analysis. Key Responsibilities: - 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. Qualification Required: - Strong background in Data Science & Analytics, including Statistical Modelling, Forecasting, Time-Series Analysis, Machine Learning, Model Explainability & Interpretation, Feature Engineering, Hypothesis Testing, and Predictive Analytics. - Proficiency in Programming with Python and SQL. - Familiarity with Libraries such as Pandas, NumPy, Scikit-learn, Statsmodels, Prophet, and Scipy. - Experience in Geospatial Analytics with knowledge of geospatial data analysis and modelling, preferably using GeoPandas, Shapely, or similar libraries. - Working knowledge of Cloud & Platform technologies including Databricks, Azure Data & Analytics ecosystem, Spark, and PySpark. - Preferred Experience in the Energy / Utilities domain, Forecasting, optimization, or operational analytics use cases, and communicating analytical insights to business stakeholders. - Good to Have skills in Azure ML / MLOps, and familiarity with frameworks like SHAP, LIME, or other Explainable AI frameworks. If you are passionate about leveragi