Data Scientist

Key Responsibilities: Collaborate with stakeholders across business units to identify data-driven opportunities and develop innovative business solutions. Analyze complex datasets using advanced statistical methods to uncover hidden patterns and generate actionable insights. Design and develop customized machine learning and AI algorithms tailored to specific customer requirements. Create scalable, reusable AI/ML/statistical models that can be deployed across multiple business units with minimal customization. Translate analytical findings into clear, actionable recommendations for business and product improvement. Partner with product and engineering teams throughout the project lifecycle to ensure data requirements are well-defined and properly captured. Lead product and project initiatives independently while providing technical guidance and mentorship to team members. Develop and maintain interactive dashboards using tools such as Power BI to communicate algorithm results and insights to stakeholders. Communicate complex analytical concepts effectively to audiences at all organizational levels through clear, data-driven storytelling. Technical Responsibilities: Perform comprehensive data exploration, preprocessing, feature engineering, and quality assurance using Python. Apply advanced machine learning algorithms including regression, classification, clustering, support vector machines, decision trees, random forests, and neural networks. Implement specialized analytics techniques such as design of experiments, forecasting, segmentation, and uncertainty analysis. Develop and deploy APIs and web applications using Flask or Django frameworks. Deploy models to cloud platforms such as Azure or AWS with containerization using Docker and CI/CD pipelines.

Responsibilities

Collaborate with stakeholders across business units to identify data-driven opportunities and develop innovative business solutions; Analyze complex datasets using advanced statistical methods to uncover hidden patterns and generate actionable insights; Design and develop customized machine learning and AI algorithms tailored to specific customer requirements; Create scalable, reusable AI/ML/statistical models that can be deployed across multiple business units with minimal customization; Translate analytical findings into clear, actionable recommendations for business and product improvement; Partner with product and engineering teams throughout the project lifecycle to ensure data requirements are well-defined and properly captured; Lead product and project initiatives independently while providing technical guidance and mentorship to team members; Develop and maintain interactive dashboards using tools such as Power BI to communicate algorithm results and insights to stakeholders; Communicate complex analytical concepts effectively to audiences at all organizational levels through clear, data-driven storytelling; Perform comprehensive data exploration, preprocessing, feature engineering, and quality assurance using Python; Apply advanced machine learning algorithms including regression, classification, clustering, support vector machines, decision trees, random forests, and neural networks; Implement specialized analytics techniques such as design of experiments, forecasting, segmentation, and uncertainty analysis; Develop and deploy APIs and web applications using Flask or Django frameworks; Deploy models to cloud platforms such as Azure or AWS with containerization using Docker and CI/CD pipelines

Requirements

B.E; 3 to 6 years