Data Scientist, Fraud
The Data Scientist will build, deploy, and improve fraud detection and loss management models while using statistical analysis and experimentation to shape risk strategy. The role requires fraud or financial-crimes experience, strong SQL and Python or R skills, and experience delivering quantitative work cross-functionally.
About the job
Responsibilities
- Build and improve machine learning models powering fraud detection and loss management systems.
- Work closely with Fraud Engineering and Risk Operations to move models from research into production.
- Use data to surface insights that shape fraud strategy across the business.
- Apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to risk problems in global payments.
- Collaborate cross-functionally to deliver results.
- Communicate analytical results clearly and drive business impact.
- Manage and deliver multiple projects with high attention to detail.
- Synthesize complex analyses into actionable recommendations.
Requirements
- PhD with 1–3 years, MS or MA with 2–6 years, or BS or BA with 4–8 years of data science or quantitative modeling experience.
- Experience with fraud, risk, or financial crimes.
- Proficiency in SQL and a computing language such as Python or R.
- Strong business acumen.
- Proficiency with AI tools to accelerate model development, analysis, and coding.
Nice-to-haves
- Strong knowledge and hands-on experience in several of machine learning, statistics, optimization, causal inference, and experimentation.
- Experience deploying models in production and adjusting model thresholds to improve performance.
- Experience designing, running, and analyzing complex experiments or using causal inference designs.
- A builder’s mindset and willingness to question assumptions and conventional wisdom.
- Experience with distributed tools such as Spark and Hadoop.
- A PhD or MS in a quantitative field such as Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, or Operations Research.
Skills
Python, R, SQL, Machine Learning, Statistical Modeling, Causal Inference, Optimization, Experimentation, Spark, Hadoop, Artificial Intelligence
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