AML Data Scientist — Analytics & Risk Modeling

PNC Financial Services Group; Austin (TX); On-site; USD 86,000 - 173,000; Full time

Job summary

PNC Financial Services Group is seeking a Data Scientist for the Anti-Money Laundering Analytics & Modeling team in a position that supports multiple U.S. locations including Austin, TX.

You will apply a range of ML techniques to detect patterns of suspicious activity and help build scalable models. You will work with stakeholders across the bank to identify indicators, create data sets, document models, and monitor performance, partnering with risk, compliance, and technology teams to advance

Qualifications

• Master's degree or higher in a quantitative field.
• Experience with data mining and data preparation for ML models.
• Proficiency in statistical methods, experimental design, probability theory, and sampling.
• Experience building, scaling, and optimizing ML systems with standard frameworks.
• Strong programming skills in Python, PySpark, R, and/or SQL.
• Experience with model risk governance and productionizing models.
• Ability to communicate complex analytical concepts to non-technical stakeholders.
• Ability to manage multiple projects and work across departments.

Responsibilities

• Use analytical techniques to extract information from customer, account, and transactional data.
• Participate in data set creation, analysis, reporting, model building, monitoring, and documentation.
• Communicate analytical results to senior executives and partners.
• Collaborate with multiple lines of defense and key stakeholders.

Skills

Python

PySpark

R

SQL

Education

Master's degree in quantitative field

Tools

ML frameworks

Job description

PNC Financial Services Group is seeking a Data Scientist for the Anti-Money Laundering Analytics & Modeling team in a position that supports multiple U.S. locations including Austin, TX.

You will apply a range of ML techniques to detect patterns of suspicious activity and help build scalable models. You will work with stakeholders across the bank to identify indicators, create data sets, document models, and monitor performance, partnering with risk, compliance, and technology teams to advance