Data Scientist

Role Overview: As a Data Scientist, your primary responsibility will involve leveraging your expertise in machine learning, regression, and classification modeling, along with time series forecasting. Your role will revolve around designing and implementing data-driven models to drive business revenue insights and predictions. Working on extensive datasets, you will be tasked with uncovering patterns, constructing robust models, and delivering interpretable and production-ready ML solutions. Utilizing tools such as Databricks, Azure/AWS, and Docker, you will play a crucial part in the data analytics process, with proficiency in Power BI being an added advantage. Key Responsibilities: - Demonstrate strong programming skills in Machine-Learning using tools such as scikit-learn, pandas, NumPy, and statsmodels. - Showcase proven experience in classification and regression modeling including Linear/Logistic Regression, Random Forest, Gradient Boosting, etc. - Exhibit a deep understanding of forecasting techniques like ARIMA, SARIMA, Prophet, and ensemble-based forecasting. - Engage in hands-on experience in exploratory data analysis (EDA), data preprocessing, and feature engineering. - Work effectively in Databricks or similar big data environments. - Possess proficiency in Azure ML or AWS services for seamless model deployment. - Utilize Docker for efficient containerization. - Have a strong grasp of evaluation metrics and model interpretability techniques such as SHAP and feature importance. Qualifications Required: - Strong expertise in Machine Learning, Deep Learning, and Generative AI including LLMs, prompt engineering, and fine-tuning. - Hands-on experience with Python, PyTorch, and Azure AI Services. - Familiarity with Microsoft Fabric, Power BI, and various data visualization techniques. - Excellent communication skills to liaise with business stakeholders effectively. - Ability to design and deploy AI/ML solutions in enterprise environments. Role Overview: As a Data Scientist, your primary responsibility will involve leveraging your expertise in machine learning, regression, and classification modeling, along with time series forecasting. Your role will revolve around designing and implementing data-driven models to drive business revenue insights and predictions. Working on extensive datasets, you will be tasked with uncovering patterns, constructing robust models, and delivering interpretable and production-ready ML solutions. Utilizing tools such as Databricks, Azure/AWS, and Docker, you will play a crucial part in the data analytics process, with proficiency in Power BI being an added advantage. Key Responsibilities: - Demonstrate strong programming skills in Machine-Learning using tools such as scikit-learn, pandas, NumPy, and statsmodels. - Showcase proven experience in classification and regression modeling including Linear/Logistic Regression, Random Forest, Gradient Boosting, etc. - Exhibit a deep understanding of forecasting techniques like ARIMA, SARIMA, Prophet, and ensemble-based forecasting. - Engage in hands-on experience in exploratory data analysis (EDA), data preprocessing, and feature engineering. - Work effectively in Databricks or similar big data environments. - Possess proficiency in Azure ML or AWS services for seamless model deployment. - Utilize Docker for efficient containerization. - Have a strong grasp of evaluation metrics and model interpretability techniques such as SHAP and feature importance. Qualifications Required: - Strong expertise in Machine Learning, Deep Learning, and Generative AI including LLMs, prompt engineering, and fine-tuning. - Hands-on experience with Python, PyTorch, and Azure AI Services. - Familiarity with Microsoft Fabric, Power BI, and various data visualization techniques. - Excellent communication skills to liaise with business stakeholders effectively. - Ability to design and deploy AI/ML solutions in enterprise environments.