Data Scientist

Interview Process: 1st Round Face-to-Face Interview Key Responsibilities: - Design, develop, and deploy advanced analytics solutions to address complex business challenges and improve operational efficiency. - Build predictive, prescriptive, and machine learning models that support business decision-making and process automation. - Analyze large, structured, and unstructured datasets to identify trends, patterns, and actionable insights. - Collect, cleanse, transform, and validate data from multiple enterprise sources to ensure high-quality analytical outputs. - Design and implement scalable analytical frameworks and machine learning solutions for production environments. - Develop interactive dashboards, reports, and visualizations using Power BI, Tableau, or similar tools. - Collaborate with business stakeholders, product teams, and technology teams to understand requirements and translate them into scalable analytical solutions. - Optimize machine learning models through feature engineering, model tuning, validation, deployment, and performance monitoring. - Support cloud-based analytics initiatives and contribute to the development of scalable AI and data science solutions. - Promote best practices in data science, model governance, documentation, and continuous improvement. - Stay current with emerging technologies in Artificial Intelligence, Machine Learning, and Advanced Analytics to drive innovation across the organization. Qualifications & Technical Skills: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline. - Minimum 7 years of experience in Data Science, Machine Learning, or Advanced Analytics. - Strong expertise in Advanced Analytics, Statistical Analysis, Machine Learning, Predictive Analytics, and Prescriptive Analytics. - Proficiency in Python and/or R for data analysis and model development. - Strong SQL skills with experience in data modeling, data wrangling, and feature engineering. - Hands-on experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost. - Experience developing interactive dashboards and reports using Power BI or Tableau. - Knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).