What does a successful Data Scientist, Fraud & Credit Risk - Decision Science do at Fiserv?
As a Data Scientist in Fraud & Credit Risk - Decision Science at Fiserv, you will play a pivotal role in our Global Business Solutions (Merchant business) team. You will be responsible for developing and deploying predictive ML models that automate risk controls, reducing fraud and credit risk losses while driving top-line growth. Your efforts will generate insightful analytics, build models/rules, and create data-driven solutions to manage risk and identify new opportunities. This role interfaces with internal and external stakeholders to deliver best-in class analytical solutions and supports functions including New Merchant On-boarding & Underwriting, Existing Merchant Risk Monitoring, and more.
What will you do?
- Develop and deploy ML/predictive models using internal and external data.
- Track and monitor model performance and provide analytical support for business decisions.
- Conduct complex analysis using statistical and quantitative techniques.
- Evaluate and integrate data from various sources for modeling, analytics, and reporting.
- Generate insights from data to assess key performance indicators/trends.
- Partner with business and technical SMEs to analyze and solve business problems.
- Support the transformation of risk data capabilities through advanced technology and real-time decision-making.
- Implement models and decision rules in production with IT/deployment teams. - Develop documentation to meet internal and external stakeholder requirements.
- Coach junior team members and oversee project delivery.
What you will need to have ?
- Bachelor's degree in Mathematics, Statistics, Computer Science, Engineering, or related field.
- 7+ years of experience in risk/marketing data analytics or predictive modeling. - Proficiency in SQL, Python, SAS, or other analytical tools/open-source programming languages.
- Strong technical skills and problem-solving ability.
- Excellent communication and interpersonal skills.
What would be great to have ?
- Master's degree in Mathematics, Statistics, Computer Science, Engineering, or related field.
- 10+ years of relevant experience.
- Experience in statistical/financial modelling in the FinTech/Payment’s domain. - Experience with credit bureaus and other external data sources.
-Hands-on experience with AI/Money Laundering techniques
R-10359114
