Associate Director, Data Science

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 Associate Director 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 Associate Director candidates, we look for prior experience in analytics, especially digital media and customer 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 4 years of prior professional experience.

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. An AD will also help formulate a vision for their accounts and bring that vision to life. As such, an AD 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.

• 4+ years of work experience in quantitative analysis with proven results in leveraging customer/transaction to address business objectives
• Experience with customer level data within audience models
• Bachelor's degree in a STEM or other related field, or equivalent work experience.
• Strong knowledge of Relational Databases and SQL programming.
• Highly proficient in at least one popular programming language used for Machine Learning, such as Python or R.
• Solid understanding of mathematical modeling, probability and statistics, and the design and simulation of stochastic systems.
• Familiarity with the mathematics behind important statistical learning algorithms such as ARIMA, Linear Regression, Logistic Regression, Centroid-based and Hierarchical Clustering, Principal Component Analysis (PCA), Decision Trees/Random Forest, Bayesian Inference, Markov Chain Monte-Carlo (MCMC).
• Familiarity with common advanced marketing analytics solutions such as Customer Segmentation, Marketing Mix Modeling (MMM), Multi-touch Attribution (MTA)
• Familiarity with the capabilities and mechanics of a broad range of advertising media channels
• Familiarity with the capabilities and mechanics of a broad range of marketing measurements and technologies such as Ad Servers, DSP, DMPs, CRMs, and Syndicated Research.
• Strong communication skills. Ability to speak and write professionally. Ability and comfort with presenting work by phone or within small groups.

Preferred Experience

• Knowledge of other Machine Learning techniques such as Natural Language Processing (NLP) and text mining, and Neural Networks is a plus.