Data Scientist

Responsibilities

• 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.

Qualifications

• 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.
• A knowledge in MLOps is a plus

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