RAPP NY is looking for a Data Scientist
to join our award-winning Technology team.
We are the agency absolutely, utterly, fiercely focused on the individual. We use our data, technology & creative smarts to make meaningful, connections with every single person a brand knows.
We are part of the OPMG group, which is in turn a part of Omnicom. This group also includes Critical Mass, Targetbase, Proximity, Credera, & sparks&honey & other well-known agencies.
Hint: a very famous, kryptonite-allergic news reporter used to work in this building. RAPP is located in the historic News Building at 220 E. 42nd St, where Christopher Reeves Superman was filmed.
From binge watching to business building & everything in between, we provide smart solutions for companies like Lilly, Charter, SAP, Pfizer, & American Family Insurance.
You have 5-7+ years of experience in a data science, data engineering, software engineering or statistics role. Additionally, a graduate degree is preferred. You should also have strong knowledge of R/Python programming, SQL, Cognos and/or other OLAP solutions.
You should have experience with unstructured data & APIs, & expertise in machine language/machine learning You also analyze large & small internal & external datasets to extract actionable insights & know how to develop statistical models to drive targeting, messaging & strategic decision-making.
You have experience with media mix modelling, multi touch attribution modelling, disparate data sets (customer database, marketing behavior, social data, qualitative research data, macro-economic data, etc.) to expose brand realities, market opportunities, & potential marketing strategies.
In your career, you have provided actionable insights, algorithms & computational solutions to real-world marketing challenges through the application of technology-based mathematical methods. Working with financial services or banking clients is a plus.
What else? Maybe you're a movie aficionado. Or maybe you have an impressively large vinyl collection? Wed love to know what makes you, you.