Marketing Data Scientist: CLV, Valuation & Analytics

Fairygodboss; Wilmington (DE); On-site; USD 95,000 - 173,000; Full time

Benefits offered by this job

Medical/dental/vision coverage

401(k) with PNC match

Paid holidays

Vacation days

Job summary

PNC is seeking a Data Scientist for Marketing Valuations to develop and apply valuation analytics and statistical models guiding marketing decisions. You will analyze customer, offer, and channel economics to inform prioritization, optimization, and investment decisions.

The ideal candidate combines strong analytical skills with a solid foundation in statistical modeling and data analysis, plus proficiency in Python/SQL and exposure to cloud/big data technologies.

Qualifications

• Bachelor's degree in a quantitative field; advanced degree preferred.
• Experience in data science, analytics, modeling, marketing analytics.
• Familiarity with marketing valuation concepts (CLV, offer economics, marketing measurement).
• Proficiency in Python and SQL. Exposure to cloud platforms and big data tech like Spark/Hadoop.

Responsibilities

• Develop valuation and forecasting models (e.g., NPV, CLV, offer economics).
• Apply statistical techniques to measure performance and quantify impact.
• Analyze customer and marketing data to identify trends and opportunities.
• Support end-to-end analytics: data prep, exploration, model development, validation, and monitoring.
• Collaborate cross-functionally with marketing, finance, and data teams.

Skills

Analytical Thinking

Competitive Advantages

Data Analytics

Data Mining

Data Science

Machine Learning

Education

Masters

Bachelor's degree

Tools

Python

SQL

Spark

Hadoop

Job description

PNC is seeking a Data Scientist for Marketing Valuations to develop and apply valuation analytics and statistical models guiding marketing decisions. You will analyze customer, offer, and channel economics to inform prioritization, optimization, and investment decisions.

The ideal candidate combines strong analytical skills with a solid foundation in statistical modeling and data analysis, plus proficiency in Python/SQL and exposure to cloud/big data technologies.