Data Scientist

Key Responsibilities:

• Model Development: Design, build, and deploy advanced propensity models, credit risk models, and other statistical models to address various business challenges within the banking domain.

• Data Analysis: Conduct in-depth analysis of large and complex datasets to identify trends, patterns, and anomalies.

• Analytical Solution: Develop and implement analytical solutions for banking operations, including customer segmentation, fraud detection, and performance optimization.

• Collaboration: Work closely with business stakeholders, data engineers, and other teams to understand business requirements and deliver effective data-driven solutions.

• Reporting: Present findings and model performance to both technical and non-technical audiences in a clear and concise manner.

• Innovation: Stay updated with the latest advancements in data science, machine learning, and the banking industry to propose and implement innovative solution.

Qualifications:

• Experience: Minimum of 5 years of professional experience as a Data Scientist, with a significant focus on the banking or financial services industry.

• Technical Skills:

• Proficiency in programming languages such as Python or R.

• Strong knowledge of machine learning algorithms (e.g., classification, regression, clustering).

• Experience with SQL and database management.

• Hands-on experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, Scikit-learn).

• Familiarity with big data & cloud technologies is a plus.

• Industry Knowledge: Proven experience in building and deploying models for credit risk, propensity modeling, or other relevant banking analytics projects.

• Soft Skills:

• Excellent problem-solving and analytical skills.

• Strong communication and presentation abilities.

• Ability to work independently and as part of a team in a fast-paced environment.