Data Scientist, Retail Banking - ML & Predictive Modeling

PNC; Pittsburgh (Allegheny County); On-site; USD 86,000 - 173,000; Full time

Benefits offered by this job

Health benefits

401(k) with match

Stock purchase plan

Job summary

PNC is seeking a Data Scientist for Retail Banking (ML & Predictive Modeling) to develop and optimize predictive models using large datasets. You will work with risk, strategy, analytics and business teams to translate complex findings into actionable insights.

The role emphasizes production modeling, data environments, and collaboration with cross-functional teams in a data-driven culture.

Qualifications

• Bachelor's degree in a quantitative field is required.
• 2–3 years of data science, predictive analytics or modeling experience.
• Experience building predictive models with large datasets.
• Strong knowledge of statistical modeling and ML techniques.
• Experience with credit risk, retail banking, or customer analytics models.
• Proficiency in Python, R, or SAS; SQL and big data tools are preferred.
• Experience with GitHub or version-control platforms is desired.

Responsibilities

• Perform analytical tasks on large structured and unstructured data to derive insights.
• Develop and validate predictive models and run experiments for optimization.
• Present findings to executives with data storytelling and visuals.
• Collaborate with Data Architects, Engineers, and Analysts on solutions.

Skills

Python

R

SAS

SQL

GitHub

Big Data

Data Visualization

Communication

Education

Bachelor's Degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Finance, Engineering, or related quantitative field

Master's Degree in a quantitative discipline

Tools

Teradata

Hadoop

AWS

Impala

Hive

Trino

Oracle

MySQL

PostgreSQL

TOAD

Job description

PNC is seeking a Data Scientist for Retail Banking (ML & Predictive Modeling) to develop and optimize predictive models using large datasets. You will work with risk, strategy, analytics and business teams to translate complex findings into actionable insights.

The role emphasizes production modeling, data environments, and collaboration with cross-functional teams in a data-driven culture.