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**Experience:6-15 years** **Job Location: Pan India(Metro Cities)** **Job Requirements\\*** •Experience in data science or advanced analytics roles, developing and deploying machine learning or statistical models in an enterprise environment. - Strong foundation in machine learning algorithms and statistical techniques (regression, classification, clustering, time-series forecasting, etc.), with ability to select appropriate methods for different problem types and interpret model outputs. - Proficiency in programming with Python (preferred) or R for data analysis and model development, including use of relevant libraries and frameworks (e.g., pandas, NumPy, scikit-learn, TensorFlow/PyTorch). - Experience in data manipulation and analysis, including cleaning large datasets, performing exploratory data analysis, and feature engineering to prepare data for modeling. - Familiarity with data visualization tools and techniques (e.g., Matplotlib, Seaborn, Tableau) to effectively communicate insights and tell a story with data. - Hands-on experience using ML model development tools or cloud-based ML platforms (such as AWS SageMaker, Azure ML Studio, or Google AI Platform) is a plus. - Excellent problem-solving and analytical thinking skills, with the ability to work cross-functionally and communicate complex analytical concepts to non-technical stakeholders. Experience working in agile, collaborative teams to deliver data science projects on time. •Excellent analytical skills and attention to detail, with the ability to troubleshoot data issues, ensure data quality, and document data processes. Effective communication skills to work with cross-functional teams in a fast-paced environment. Key Responsibilities\\*•Problem Definition & Data Collection: Engage with business stakeholders to understand analytical objectives and define data-driven problems. Identify and gather relevant internal and external data sources needed for analysis. - Data Analysis & Feature Engineering: Conduct thorough exploratory data analysis (EDA) to discover patterns, outliers, or anomalies. Perform feature engineering to create new variables that improve model performance, ensuring data quality and consistency throughout. - Model Development: Develop and refine predictive models and machine learning algorithms to address specific business questions (such as customer segmentation, predictive maintenance, anomaly detection, etc.). Train, test, and fine-tune models using appropriate validation techniques to ensure robustness. - Insight Generation & Visualization: Translate complex model results into meaningful insights and actionable recommendations. Create visualizations and reports to communicate findings clearly to non-technical audiences, highlighting key patterns, trends, and potential business impacts. - Deployment & Monitoring: Work closely with data engineering and IT teams to deploy models into production (as APIs or batch processes) and integrate them into existing systems or products. Monitor model performance over time and retrain or update models as needed to maintain accuracy and relevance. - Research & Innovation: Stay updated with the latest advancements in data science and AI, such as new algorithms, libraries, or techniques. Experiment with emerging tools and methods, and bring innovative ideas to continuously improve the organizations data science capabilities.