Sr. Data Scientist

Posted 5mo ago

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 solutions.

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

Good understanding on building performance dashboards, sales models (propensity, segmentation, income proxy), credit models (A-score, B-score)
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.

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

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.

This position will be assigned to an FSI client's project.

Skills