Manager, Data Analytics & Audience Solutions

Job Description

What does Zenith Data Sciences do?

Working closely with both the Planning and Analytics teams, Data Sciences designs and implements statistical models and machine learning solutions that tie our clients’ marketing to real-world business goals.

We use these models to understand past performance, predict future performance, and inform and optimize future decisions.

Our work brings our clients closer to their marketing, helping them understand if they are talking to the right people in the right way.

What does a successful Manager of Data Science look like?

We don’t all look the same, and we don’t expect you to, either. But successful members of our team generally have a passion for emerging tech and media, bring a data-driven approach to decision-making, have a fresh perspective and share it in a positive way, don’t shy away from a challenge, and are always hungry to learn more.

Among Manager candidates, we look for prior experience in media analytics, especially digital media and audiences segmentation / modeling.

You should be seasoned at representing your team to external clients and internal leadership. We hope you have a love for numbers and know how to bring data to life through a compelling story.

We’re also hoping you have at least 2-3 years of experience managing team members and have at least 5 years of prior professional experience.

What does a Manager of Data Science do day-to-day?

You will work very closely with planning, audience strategy and analytics teams to help them solve marketing problems. They also contribute crucial intellectual capital to the data science team by sharing knowledge and designing models and Data Science solutions based on their clients’ business needs and using data to tell great stories.

A Manager will also help formulate a vision for their accounts and bring that vision to life. As such, a Manager is a mentor, manager, project manager, and thought leader, all at the same time.

Day-to-day responsibilities include :

Design, estimate, tune, score and maintain advanced statistical and mathematical models (e.g. classification, numeric forecasts, customer segmentation, customer propensity, attribution, etc.).

Produce accurate statistical analysis and ensure high quality of the data analysis produced.

Interpret, document and present / communicate analytical results to multiple business disciplines, providing conclusions and recommendations.

Take analytical objectives and define data requirements. Extract, clean, and transform both customer level, and aggregated data for analysis, modelling, segmentation and reporting.