Data Collection and Cleaning: Collect, extract, and clean relevant data from various internal and external sources, ensuring data quality and integrity.
Data Analysis and Modeling: Apply statistical and machine learning techniques to analyze data, build predictive models, and uncover meaningful insights that drive decision-making in areas such as risk management, customer behavior analysis, and fraud detection.
Development and Deployment of Data Solutions: Design, develop, and deploy end-to-end data solutions that involve data ingestion, transformation, and visualization. This may include building data pipelines, implementing APIs, and creating interactive dashboards for stakeholders.
Model Development and Validation: Develop and validate predictive models for credit scoring, risk assessment, customer segmentation, and other banking-related applications. Ensure models comply with regulatory requirements and best practices.
Collaborative Problem Solving: Work closely with cross-functional teams, such as business stakeholders, IT professionals, and compliance officers, to understand their requirements, address their data-related challenges, and provide data-driven solutions.
Automation and Efficiency: Identify opportunities for process automation and optimization within the bank’s operations, leveraging data-driven techniques to streamline workflows, improve efficiency, and reduce operational costs.
Continuous Learning and Innovation: Stay up-to-date with the latest advancements in data science, machine learning, and financial technologies. Identify and explore innovative solutions that can enhance the bank’s analytical capabilities and competitive edge.
Communication and Visualization: Effectively communicate complex data insights to non-technical stakeholders through visualizations, reports, and presentations. Translate technical findings into actionable recommendations for business teams.
3-5 years of experience
A BCS degree in computer science, data science, Engineering, Statistics, or Econometrics.
Experience in Programming and software development (preferably in Python).
A good understanding of Data analysis and modeling
Familiarity with data technologies
Solid understanding of Machine Learning models and concepts.