Data Scientist

Role Overview: You will be a Data Scientist with expertise in Python, Statistical Modelling, Forecasting, and Machine Learning to develop advanced analytics solutions using Azure and Databricks. Your main responsibilities will include performing data analysis, developing predictive models, implementing statistical forecasting, building geospatial analytics solutions, and communicating insights to stakeholders. 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 modeling 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 skills in Data Science & Analytics, Statistical Modelling, and Forecasting. - Proficiency in Time-Series Analysis, Machine Learning, and Model Explainability. - Experience in Feature Engineering, Hypothesis Testing, and Predictive Analytics. - Proficient in programming with Python and SQL. - Familiarity with libraries such as Pandas, NumPy, Scikit-learn, Statsmodels, Prophet, and Scipy. - Experience with geospatial data analysis and modeling using GeoPandas, Shapely, or similar libraries (preferred). - Knowledge of Cloud & Platform technologies like Databricks, Azure Data & Analytics ecosystem, and Spark / PySpark. - Preferred experience in the Energy / Utilities domain, forecasting, optimization, or operational analytics use cases. - Ability to communicate analytical insights to business stakeholders. - Good to have experience with Azure ML / MLOps and Explainable AI frameworks like SHAP or LIME. If you are keen to join, you will be part of an organization that values your contributions, recognizes your potential, and provides ample opportunities for growth. Role Overview: You will be a Data Scientist with expertise in Python, Statistical Modelling, Forecasting, and Machine Learning to develop advanced analytics solutions using Azure and Databricks. Your main responsibilities will include performing data analysis, developing predictive models, implementing statistical forecasting, building geospatial analytics solutions, and communicating insights to stakeholders. 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 modeling 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 skills in Data Science & Analytics, Statistical Modelling, and Forecasting. - Proficiency in Time-Series Analysis, Machine Learning, and Model Explainability. - Experience in Feature Engineering, Hypothesis Testing, and Predictive Analytics. - Proficient in programming with Python and SQL. - Familiarity with libraries such as Pandas, NumPy, Scikit-learn, Statsmodels, Prophet, and Scipy. - Experience with geospatial data analysis and modeling using GeoPandas, Shapely, or similar libraries (preferred). - Knowledge of Cloud & Platform technologies like Databricks, Azure Data & Analytics ecosystem, and Spark / PySpark. - Preferred experience in the Energy / Utilities domain, forecasting, optimization, or operational analytics use cases. - Ability to communicate analytical insights to business stakeholders. - Good to have experience with Azure ML / MLOps and Explainable AI frameworks like SHAP or LIME. If you are keen to join, you will be part of an organization that values your contributions, recognizes your potential, and provides ample opportunities for growth.