AML Analytics Scientist: ML & Risk Modeling

PNC; Wilmington (DE); On-site; USD 86,000 - 173,000; Full time

Benefits offered by this job

Medical/prescription coverage

401(k) with company match

Paid holidays

Vacation days

Job summary

PNC’s Anti-Money Laundering Analytics & Modeling team seeks a Data Scientist to build and optimize models detecting suspicious activity. Based in multiple hubs (Pittsburgh, Philadelphia, Wilmington, etc.), you’ll apply ML algorithms, collaborate with stakeholders, and help enhance risk controls across datasets.

You will lead projects using logistic regression, clustering, boosting and neural networks, with strong Python/PySpark/R/SQL skills, and alignment with model governance and deployment

Qualifications

• Master’s degree or higher in a quantitative field.
• Experience with data mining and data preparation for ML models.
• Proficiency in statistical methods and tools, including experimental design, probability theory, and sampling.
• Expertise in building, scaling, and optimizing ML systems with industry recognized frameworks and algorithms.
• Strong programming skills in Python, PySpark, R, and/or SQL.

Responsibilities

• Perform analytical tasks on large structured and unstructured data to extract actionable business insights.
• Participate in data gathering, processing and mining of large datasets.
• Develop algorithms using ML techniques to predict outcomes and advise management.
• Run experiments methodically to identify opportunities for product and process optimization and present insights.
• Collaborate with Data Architects, Analysts, Engineers and Visualization Experts on data-driven solutions.

Skills

Analytical Thinking

Competitive Advantages

Data Analytics

Data Mining

Data Science

Machine Learning

Python

PySpark

R

SQL

Education

Masters

Tools

AWS

Google Cloud

Azure

Job description

PNC’s Anti-Money Laundering Analytics & Modeling team seeks a Data Scientist to build and optimize models detecting suspicious activity. Based in multiple hubs (Pittsburgh, Philadelphia, Wilmington, etc.), you’ll apply ML algorithms, collaborate with stakeholders, and help enhance risk controls across datasets.

You will lead projects using logistic regression, clustering, boosting and neural networks, with strong Python/PySpark/R/SQL skills, and alignment with model governance and deployment