Zensar Technologies—Data Scientist-3

Job description

Description: The ideal candidate should have hands-on experience across the complete data science lifecycle, including data exploration, feature engineering, model development, evaluation, deployment, monitoring, and optimization. Requirements: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Engineering, or a related discipline. - 5+ years of relevant experience in Data Science, Machine Learning, or advanced analytics. - Strong analytical and problem-solving capabilities. - Excellent communication and stakeholder-management skills. - Ability to translate business problems into appropriate data science and AI/ML solutions. - Experience working in Agile and enterprise development environments. - Banking, payments, financial services, or other large-scale enterprise experience is preferred. Responsibilities: - Analyze large structured and unstructured datasets to identify patterns, trends, and actionable insights. - Develop and implement machine learning and statistical models for business use cases. - Perform data cleaning, preprocessing, exploratory data analysis, and feature engineering. - Build predictive models using supervised and unsupervised learning techniques. - Develop solutions for classification, regression, clustering, forecasting, anomaly detection, and recommendation use cases. - Use Python and SQL for data manipulation, analysis, and model development. - Develop and optimize ML models using libraries such as Scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch. - Design appropriate model validation and evaluation strategies using relevant performance metrics. - Build reusable data science pipelines and production-ready ML solutions. - Collaborate with Data Engineers, ML Engineers, Software Engineers, Product Owners, and business stakeholders. - Work with large-scale data processing platforms and cloud environments. - Deploy machine learning models through APIs, batch processing, or real-time inference pipelines. - Implement MLOps practices covering model versioning, deployment, monitoring, retraining, and governance. - Develop prototypes and proof-of-concepts for new AI/ML use cases. - Present complex analytical findings to both technical and non-technical stakeholders. - Ensure data quality, security, privacy, explainability, and responsible AI practices.