Data Scientist (Banking Domain)

As a Senior Data Scientist in the banking domain, your role will involve building and deploying scalable ML models for various use cases such as fraud detection, credit risk modeling, customer analytics, and transaction intelligence using large-scale enterprise datasets. You will work with a team of Data Engineers, BI teams, and business stakeholders to ensure model performance, scalability, and compliance with banking data governance and regulatory standards. Key Responsibilities: - Develop and deploy machine learning models for banking use cases including fraud detection, customer segmentation, credit risk modeling, and transaction analytics. - Perform data preprocessing, feature engineering, and exploratory data analysis. - Build predictive models to drive customer growth, risk monitoring, and operational efficiency. - Collaborate with cross-functional teams and business stakeholders. - Deploy and monitor ML models in production environments. - Present insights and recommendations to business teams. Qualifications Required: - Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or a related field. Mandatory Skills: - Strong proficiency in Python and SQL. - Hands-on experience with ML frameworks such as Scikit-learn, TensorFlow, and PyTorch. - Strong knowledge of statistics, predictive modeling, and feature engineering. - Experience with large datasets, SQL-based systems, and model deployment in production. - Exposure to Cloud platforms like AWS, Azure, or GCP. - Strong data visualization and storytelling skills. - Experience working with banking or financial datasets. Technical Skills: - Programming: Python, SQL. - Machine Learning: Scikit-learn, TensorFlow, PyTorch. - Data Processing: Pandas, NumPy, PySpark/Spark. - Visualization: Matplotlib, Seaborn, Plotly, Power BI, Tableau. - Data Platforms: Databricks, Snowflake, Data Lakes. - Cloud: AWS, Azure, GCP. - MLOps: Docker, Kubernetes, ML pipelines. Good to Have Skills: - Experience with Apache Airflow, Kafka, real-time or streaming data pipelines. - Exposure to generative AI, LLM frameworks, reinforcement learning, deep learning. - Knowledge of AML, fraud detection, regulatory reporting. - Certifications in Databricks, Snowflake, Dataiku. Key Competencies: - Solid analytical and problem-solving skills. - Excellent communication and presentation skills. - Ability to translate complex data into business insights. - Collaborative mindset and stakeholder management. - Adaptability in fast-paced environments. KPIs / Success Metrics: - Model accuracy and predictive performance. - On-time delivery of ML solutions. - Business impact of deployed models. - Production model stability and uptime. - Stakeholder satisfaction. As a Senior Data Scientist in the banking domain, your role will involve building and deploying scalable ML models for various use cases such as fraud detection, credit risk modeling, customer analytics, and transaction intelligence using large-scale enterprise datasets. You will work with a team of Data Engineers, BI teams, and business stakeholders to ensure model performance, scalability, and compliance with banking data governance and regulatory standards. Key Responsibilities: - Develop and deploy machine learning models for banking use cases including fraud detection, customer segmentation, credit risk modeling, and transaction analytics. - Perform data preprocessing, feature engineering, and exploratory data analysis. - Build predictive models to drive customer growth, risk monitoring, and operational efficiency. - Collaborate with cross-functional teams and business stakeholders. - Deploy and monitor ML models in production environments. - Present insights and recommendations to business teams. Qualifications Required: - Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or a related field. Mandatory Skills: - Strong proficiency in Python and SQL. - Hands-on experience with ML frameworks such as Scikit-learn, TensorFlow, and PyTorch. - Strong knowledge of statistics, predictive modeling, and feature engineering. - Experience with large datasets, SQL-based systems, and model deployment in production. - Exposure to Cloud platforms like AWS, Azure, or GCP. - Strong data visualization and storytelling skills. - Experience working with banking or financial datasets. Technical Skills: - Programming: Python, SQL. - Machine Learning: Scikit-learn, TensorFlow, PyTorch. - Data Processing: Pandas, NumPy, PySpark/Spark. - Visualization: Matplotlib, Seaborn, Plotly, Power BI, Tableau. - Data Platforms: Databricks, Snowflake, Data Lakes. - Cloud: AWS, Azure, GCP. - MLOps: Docker, Kubernetes, ML pipelines. Good to Have Skills: - Experience with Apache Airflow, Kafka, real-time or streaming data pipelines. - Exposure to generative AI, LLM frameworks, reinforcement learning, deep learning. - Knowledge of AML, fraud detection, regulatory reporting. - Certifications in D