Data Scientist

Role Overview: You will be responsible for building, validating, and maintaining machine learning models for various purposes such as forecasting, classification, regression, anomaly detection, and business analytics. Additionally, you will perform data cleaning, feature engineering, model evaluation, and performance monitoring to ensure the reliability and scalability of solutions. You will also develop data pipelines, automation workflows, and analytical solutions using Python and SQL. Moreover, your role will involve analyzing business data to identify trends, risks, opportunities, and key performance drivers and translating these findings into actionable recommendations. Collaboration with various teams to understand requirements and deliver measurable analytical outcomes will be essential. Effective communication of model assumptions, limitations, and results to both technical and non-technical stakeholders will also be a key aspect of your responsibilities. Furthermore, you will own data science projects end-to-end, including requirement gathering, development, deployment, and stakeholder management. Mentoring junior team members and contributing to improving data science best practices across the organization will also be part of your role. Key Responsibilities: - Build, validate, and maintain machine learning models for forecasting, classification, regression, anomaly detection, and business analytics. - Perform data cleaning, feature engineering, model evaluation, and performance monitoring. - Develop data pipelines, automation workflows, and analytical solutions using Python and SQL. - Analyze business data to identify trends, risks, opportunities, and key performance drivers. - Collaborate with business, operations, finance, and technology teams to understand requirements and deliver measurable analytical outcomes. - Communicate model assumptions, limitations, and results effectively to both technical and non-technical stakeholders. - Own data science projects end-to-end, including requirement gathering, development, deployment, and stakeholder management. - Mentor junior team members and contribute to improving data science best practices across the organization. Qualification Required: - Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, Finance, or a related quantitative discipline. - 4+ years of experience in Data Science, Machine Learning, Predictive Analytics, or Statistical Modeling. - Strong programming skills in Python and SQL with hands-on experience building production-ready analytical workflows. - Solid understanding of regression, classification, clustering, forecasting, anomaly detection, feature engineering, and model evaluation techniques. - Experience with machine learning libraries such as Scikit-learn, XGBoost, LightGBM, Statsmodels, TensorFlow, or PyTorch. - Strong communication, stakeholder management, problem-solving, and project ownership skills. [Note: No additional details of the company were present in the provided job description.] Role Overview: You will be responsible for building, validating, and maintaining machine learning models for various purposes such as forecasting, classification, regression, anomaly detection, and business analytics. Additionally, you will perform data cleaning, feature engineering, model evaluation, and performance monitoring to ensure the reliability and scalability of solutions. You will also develop data pipelines, automation workflows, and analytical solutions using Python and SQL. Moreover, your role will involve analyzing business data to identify trends, risks, opportunities, and key performance drivers and translating these findings into actionable recommendations. Collaboration with various teams to understand requirements and deliver measurable analytical outcomes will be essential. Effective communication of model assumptions, limitations, and results to both technical and non-technical stakeholders will also be a key aspect of your responsibilities. Furthermore, you will own data science projects end-to-end, including requirement gathering, development, deployment, and stakeholder management. Mentoring junior team members and contributing to improving data science best practices across the organization will also be part of your role. Key Responsibilities: - Build, validate, and maintain machine learning models for forecasting, classification, regression, anomaly detection, and business analytics. - Perform data cleaning, feature engineering, model evaluation, and performance monitoring. - Develop data pipelines, automation workflows, and analytical solutions using Python and SQL. - Analyze business data to identify trends, risks, opportunities, and key performance drivers. - Collaborate with business, operations, finance, and technology teams to understand requirements and deliver measurable analytical outcomes. - Communicate model assumptions, limitations, and r