We are looking for a Data Scientist with strong Banking/Financial Services domain experience to develop data-driven solutions for banking use cases. The ideal candidate should have strong expertise in Python, Machine Learning, SQL, statistical modelling, and hands-on experience working with banking data and business problems.

Develop and implement Machine Learning and statistical models for banking use cases. Analyze large and complex datasets to identify trends, patterns, risks, and business opportunities. Work on customer analytics, credit risk, fraud detection, propensity modelling, segmentation, and predictive analytics. Build, validate, tune, and deploy ML models into production. Perform feature engineering, model selection, model evaluation, and hyperparameter tuning. Develop data pipelines and prepare datasets for analytical and ML applications. Work closely with banking, product, risk, technology, and business teams to translate business problems into data science solutions. Monitor model performance and support model improvement and retraining. Present analytical findings and model outcomes to technical and business stakeholders. Ensure solutions follow appropriate data privacy, security, governance, and regulatory requirements.

3–8 years of experience in Data Science / Machine Learning. Strong Banking / Financial Services domain experience is mandatory. Strong programming skills in Python. Excellent SQL and experience working with large datasets. Hands-on experience with Machine Learning algorithms and statistical modelling. Experience with libraries such as Pandas, NumPy, Scikit-learn. Strong understanding of classification, regression, clustering, forecasting, and model evaluation. Experience in feature engineering and predictive modelling. Good understanding of data preprocessing, exploratory data analysis, and statistical concepts. Experience with Jupyter, Git, and ML development/deployment practices. Strong analytical, problem-solving, and communication skills.