Data Scientist - Anti-Money Laundering Analytics & Modeling
About The Position
As a Data Scientist in PNC's Anti-Money Laundering Analytics & Modeling organization, you will work with a team using statistical techniques to build models that detect, monitor, and avert concerning patterns of account activity. You will partner with stakeholders across the bank to identify patterns and risk indicators in account and transaction data, find opportunities for new strategies, and recommend improvements to existing strategies. The role includes leading innovative AML projects and using methods such as logistic regression, clustering, gradient boosting, neural networks, and other machine learning algorithms to design samples and build statistical models. The position performs analytical work on structured and unstructured data to produce actionable business insights, participates in gathering, processing, and mining large datasets, develops algorithms to predict business outcomes and recommend actions, and runs analytical experiments to identify product and process improvements. The role may partner with data architects, analysts, engineers, and visualization experts to develop data-driven business solutions. This is an in-office position based in one of the listed locations: Pittsburgh or Philadelphia, Pennsylvania; Wilmington, Delaware; Cleveland, Ohio; Charlotte, North Carolina; Austin, Texas; or Washington, DC. PNC does not provide employment visa sponsorship or participate in STEM OPT for this position.
Requirements
- University or college degree, or a comparable combination of education, job-specific certifications, and experience; the listed education level is master's.
- At least 3 years of relevant or direct industry experience is typical for roles at this level.
- Demonstrated knowledge, skills, and abilities appropriate to the role.
- Ability to perform analytical tasks on structured and unstructured data and extract actionable business insights.
- Ability to gather, process, and mine large and complex datasets.
- Ability to develop algorithms using advanced mathematical and statistical techniques, including machine learning.
- Customer-focused approach and ability to prioritize customer needs and satisfaction in business decisions.
- Ability to assess and manage risks in support of PNC's Enterprise Risk Management Framework.
Nice To Haves
- Master's degree or higher in a quantitative field.
- Experience with data mining and preparing data for machine-learning models.
- Proficiency in statistical methods and tools, including experimental design, probability theory, and sampling.
- Expertise building, scaling, and optimizing machine-learning systems using recognized frameworks and algorithms.
- Strong programming skills in Python, PySpark, R, and/or SQL.
- Experience with model risk governing bodies during model validation and with implementation partners during productionization.
- Critical thinking and problem-solving skills for complex business problems.
- Ability to explain complex technical concepts clearly to non-technical stakeholders and business partners.
- Ability to manage multiple projects simultaneously.
- Strong teamwork skills and ability to work across departments.
- Experience in banking or financial services.
- Experience with anti-fraud and/or anti-money-laundering modeling.
- Hands-on experience building AI/ML models, including neural networks.
- Experience with cloud platforms such as AWS, Google Cloud, or Azure.
- Certifications are often desired.
Responsibilities
- Extract usable information from customer, account, transactional, and other data sources using analytical techniques.
- Participate in dataset creation, analysis, reporting, model building, model monitoring, and model documentation.
- Communicate analytical results and represent the modeling team in forums to update senior executives and team partners on modeling efforts.
- Collaborate with first, second, and third lines of defense and other stakeholders.
- Lead AML modeling projects and use statistical and machine-learning methods to design samples and build models.
- Gather, process, and mine large and complex datasets; develop algorithms to predict business outcomes and recommend actions.
- Run analytical experiments to identify opportunities for product and process optimization.
- Assist in presenting business insights using visualization technologies and data storytelling.
- Partner as needed with data architects, data analysts, data engineers, and visualization experts to develop data-driven solutions.
- Manage risks associated with business objectives and activities in support of PNC's Enterprise Risk Management Framework.
Benefits
- Medical and prescription drug coverage, including a Health Savings Account feature
- Dental and vision options
- Employee and spouse/child life insurance
- Short- and long-term disability protection
- 401(k) with PNC match
- Pension plan
- Stock purchase plans
- Dependent care reimbursement account
- Back-up child and elder care
- Adoption, surrogacy, and doula reimbursement
- Educational assistance, including select fully paid programs
- Wellness program with financial incentives
- Maternity and/or parental leave
- Up to 11 paid holidays each year
- 9 occasional absence days each year, unless otherwise required by law
- 15 to 25 vacation days each year, depending on career level and years of service
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
