Data Scientist - Corporate & Institutional Banking

PNC•Pittsburgh, PA

7d•$86,250 - $172,500•Onsite

About The Position

As a Data Scientist within PNC's Corporate and Institutional Banking (C&IB) organization, you will be based in Pittsburgh or Philadelphia, PA, Cleveland, OH, Birmingham, AL, Wilmington, DE, Charlotte, NC, or Houston, TX. PNC is seeking a Data Scientist to join the Corporate and Institutional Banking (C&IB) team. In this role, you will work closely with business stakeholders, product managers, and engineering teams to explore data, develop interpretable machine learning and analytical solutions, and deliver those solutions into production to address complex business problems and support C&IB’s growth objectives. The team’s work spans key business verticals including Sales, Credit and Underwriting, and Operations, providing opportunities to apply analytics across the full lifecycle of client engagement, risk decisioning, and operational execution. The ideal candidate combines strong analytical problem solving skills with practical experience in data analysis, machine learning, and Generative AI, and is able to clearly communicate insights and translate business needs into scalable, production ready analytical solutions and data products.

Requirements

2–3 years of relevant, post‑graduate professional experience as a Data Scientist or in a comparable analytics role.
Ability to design and develop interactive dashboards to communicate, visualize, and monitor analytical results using Python or R–based frameworks (e.g. R Shiny, Dash, Flask)
Strong programming experience in Python or R.
Strong SQL skills and experience working with large datasets.
Experience working with Apache Spark using one or more languages (e.g. PySpark, sparklyr, or Spark SQL).
Experience with Git or comparable version control tools.
Machine Learning: Experience developing and evaluating traditional ML models, including feature engineering and performance assessment.
Generative AI: Experience building applied GenAI solutions, including familiarity with retrieval augmented generation and related architectural approaches.
Experience producing delivery artifacts such as business requirements, user stories, and test cases
Experience supporting testing, validation, and transitions from analytical prototype to production
Master’s degree in quantitative fields (Computer Science, Data Science, Machine Learning, Mathematics, Statistics), or Bachelor’s degree with equivalent practical experience

Nice To Haves

Exposure to business domains such as credit, accounting, or financial operations.
Familiarity with underwriting concepts or a willingness to learn them on the job.
Experience working across multiple business areas or interest in developing cross‑domain expertise.
Exposure to entity resolution or record‑linkage problems, including matching, deduplication, or linking entities across disparate internal or external data sources.

Responsibilities

Use Python or R to explore data, perform analysis, and rapidly prototype analytical approaches within repeatable workflows.
Design, develop, validate, and monitor interpretable machine learning models using sound statistical and modeling techniques.
Own the end‑to‑end delivery of analytical solutions—from prototype through testing, validation, and scalable production deployment—collaborating closely with engineering and testing partners to ensure production readiness.
Act as a key communication bridge across business, product, and engineering teams to gather and document requirements, clearly communicate analytical solutions, and ensure business needs are accurately implemented.
Define and track performance metrics to measure solution effectiveness and business impact.
Performs analytical tasks on vast amounts of structured and unstructured data to extract actionable business insights.
Participates in the data gathering, data processing and data mining of large and complex datasets.
Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
Assists in the presentation of business insights to management using visualization technologies and data storytelling.
May partner with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.

Benefits

medical/prescription drug coverage (with 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 and stock purchase plans
dependent care reimbursement account
back-up child/elder care
adoption, surrogacy, and doula reimbursement
educational assistance, including select programs fully paid
a robust 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
between 15 to 25 vacation days each year, depending on career level; and years of service