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Digital Marketing Analytics

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We are looking for an individual contributor who will transform vast amounts of data into actionable insights and influence marketing strategy.

This role provides decision support to marketing teams in areas such as:

  • Measure attribution of business outcomes to media activity (across tactics, messages, reach vs frequency) to improve media efficiency and conversions

  • Build predictive and segmentation models to understand customer behavior

  • Conduct regular and ad-hoc analyses, data mining and predictive analytics projects to provide leadership with actionable insights at tactical and strategic levels

  • Initiate new model development and areas for further research and analysis



  • Define and develop approaches to conduct descriptive, predictive, and prescriptive analyses on large data sets

  • Build models using statistical tools to address business questions; Have a deep understanding of model inputs, outputs, and mechanics to interpret and validate model results

  • Consider and control for media consumption, consumer behavior, and external forces that drive business outcomes (seasonality, competitive landscape, trends, etc.)

  • Thoughtfully construct the “right” objective to optimize

  • Work cross-functionally and collaborate with analytical teams, marketing, other business partners, and external vendors

  • Question results and find meaningful insights; not just report numbers

  • Communicate findings through data visualization and storytelling; explain work effectively to any audience

  • Influence and convince stakeholders that the data and analytics are sound, and to take the recommended action

  • Be a thought leader in building and improving methods


Skills & Experience:

  • Bachelor’s degree in a quantitative field (Applied Mathematics, Statistics, Computer Science, Engineering, etc.); Master’s degree in a quantitative field (see above), PhD preferred

  • Less than 5 years experience (junior), 5+ years (mid-senior) experience in an advanced data analytics or data science role, with a focus on project scoping, execution, and communication of findings

  • Statistical modeling (hypothesis testing, causal modeling, data mining), optimization algorithms

  • Experience with data science techniques such as machine learning, natural language processing (NLP), deep learning, data munging (data wrangling), bayesian methods, decision trees, random forests, clustering

  • Experience working with large databases and big data

  • Knowledge of tools: SQL, R, SAS, SPSS, Tableau, Excel, PowerPoint

  • Knowledge of data science tools like Hadoop, Hive, RapidMiner, Spark, Anaconda

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