Data Scientist-3

The ideal candidate should have hands-on experience across the complete data science lifecycle, including dataexploration,featureengineering,modeldevelopment,evaluation,deployment,monitoring,andoptimization.

• Analyze large structured and unstructured datasets to identify patterns, trends, and actionable insights.
• Develop and implement machinelearningandstatisticalmodels 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 PythonandSQL for data manipulation, analysis, and model development.
• Develop and optimize ML models using libraries such as Scikit-learn,Pandas,NumPy,TensorFlow,orPyTorch.
• 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.
• Bachelor's or Master's degree in ComputerScience,DataScience,Statistics,Mathematics,ArtificialIntelligence,MachineLearning,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 datascienceandAI/MLsolutions.
• Experience working in Agile and enterprise development environments.
• Banking, payments, financial services, or other large-scale enterprise experience is preferred.