Data Scientist

Perform exploratory data analysis on large-scale banking datasets to identify trends, patterns, and opportunities for business impact.
Utilize Azure Synapse Analytics and Power BI to create dashboards and reports, enabling stakeholders to make data-informed decisions.
Model Development & Experimentation

Build and validate predictive models using techniques such as regression, classification, and clustering for use cases like credit scoring, fraud detection, and customer segmentation.
Experiment with advanced analytics like deep learning, NLP, and time-series forecasting to address specific banking challenges.
Data Preparation & Feature Engineering

Develop data pipelines and perform ETL processes using Azure Data Factory to prepare structured and unstructured data for analysis.
Conduct feature engineering, data transformation, and data cleaning to improve model accuracy and robustness.
Collaboration & Business Alignment

Collaborate with stakeholders to understand business needs and translate them into data science solutions.
Work closely with data engineers, machine learning engineers, and other team members to ensure seamless integration of models into production.
Deployment & Monitoring

Deploy models to Azure Machine Learning Service and set up monitoring to evaluate model performance and make adjustments as needed.
Implement best practices in MLOps for model lifecycle management and versioning.
Skills:
Proficiency in Python and ML libraries (e.g., Scikit-Learn, TensorFlow)
Solid SQL skills and experience with Azure SQL Database or Azure Cosmos DB
Strong understanding of statistical analysis, predictive modeling, and banking use cases (e.g., risk scoring, fraud detection)
Familiarity with Power BI for data visualization