Data Scientist - Credit Strategy

Note- NO C2C On this role. The client will be paying the candidate directly.

MUST HAVE:

• Bachelor s degree in computer science, Engineering, Mathematics, Statistics, Data Mining or related field or equivalent practical experience.
• Minimum 2 years of experience in risk analytics, data analysis, or data science within the Fintech or online payments industry.
• Experience using statistics and data science to solve complex business problems.
• Proficiency in SQL, Python, Excel including key data science libraries.
• Experience working with large datasets.
• Ability to clearly communicate complex results to technical experts, business partners, and executives including development of dashboards and visualizations, i.e. Tableau.
• Desirable to have experience or aptitude solving problems related to risk using data science and analytics.
• Experience with AWS, payment rule systems, and knowledge of credit products.

Notes from Hiring Manager:

• Interview process: Multiple (At home screening, 1 technical interview with senior team member, 1 interview panel with hiring manager and team member).
• Team size: 5 team members, position will work closely with a senior team member at outset, role will largely focus on reporting, so experience with visualization and data storytelling is a plus.

JOB DESCRIPTION:

Seeking a talented, enthusiastic and dedicated person to join the Credit Risk Strategy team. The incumbent will be responsible for leading key projects associated with credit risk assessment, data analysis and loss mitigation. This position requires a person who has experience performing analytics, refining risk strategies, and developing predictive algorithms preferably in the credit risk domain.

Key Job Functions

• Design rules to detect/mitigate loss
• Investigate novel/large cases
• Identify root cause
• Set strategy for different risk types
• Work with product/engineering to improvement control capabilities
• Develop and present strategies and guide execution
• Drive results that maximize eligible customers while controlling losses

Professional Experience/Background to be successful in this role:

• Minimum 2 years of experience in risk analytics, data analysis, or data science within the Fintech or online payments industry.
• Bachelor s degree in computer science, Engineering, Mathematics, Statistics, Data Mining or related field or equivalent practical experience.
• Experience using statistics and data science to solve complex business problems.
• Proficiency in SQL, Python, Excel including key data science libraries.
• Experience working with large datasets.
• Ability to clearly communicate complex results to technical experts, business partners, and executives including development of dashboards and visualizations, ie Tableau.
• Comfortable with ambiguity and yet able to steer analytics projects toward clear business goals, testable hypotheses, and action-oriented outcomes.
• Demonstrated analytical thinking through data-driven decisions, as well as the technical know-how, and ability to work with your team to make a big impact.
• Desirable to have experience or aptitude solving problems related to risk using data science and analytics.
• Bonus: Experience with AWS, payment rule systems, and knowledge of credit products.

Expected Outcome in 6-12 months:

• Development of dashboard and visualizations to track KPI of credit strategies implemented
• Work closely with team members and stakeholders to consult, design, develop, and manage credit trends and losses that not only solve emerging loss trends but also provide a great experience to end customers.
• Utilize data analysis to design and implement credit strategies
• Analyze and transform data using RaaS platforms to identify loss patterns and optimize the systems to flag these patterns
• Collaborate with cross-functional stakeholders including product managers and engineering teams to deploy data-driven credit solutions that operate at scale and in real time for end customers.
• Make business recommendations to leadership and cross-functional teams with effective presentations of findings at multiple levels of stakeholders.

Preferred Skills:

• Data analytics
• Rule development
• Dashboard Creation
• Project Management
• Strong Communication