Data-Driven Marketing Strategist

1 week ago

San Mateo, California, United States PlayStation Full time
About the Role

We are seeking a highly skilled Data-Driven Marketing Strategist to join our team at PlayStation. As a key member of our marketing organization, you will play a crucial role in driving business growth and delivering impactful insights to our stakeholders.

Key Responsibilities
  • Develop a comprehensive KPI framework to measure incremental return on investment across paid and owned marketing channels, ensuring its acceptance across key marketing, finance, and business leaders.
  • Determine the optimal marketing budget required to achieve business goals by analyzing campaign performance data, market trends, and consumer behavior.
  • Design and implement a test and learn framework to assess the influence of our marketing endeavors, including the development of statistical models to measure the effectiveness of our marketing efforts.
  • Collaborate closely with finance, marketing, and business leaders to ensure the developed framework is accepted and adopted across the organization, and to provide insights on the most effective allocation of resources.
  • Analyze large datasets to determine the optimal budget allocation required to achieve business goals across all PlayStation lines of business, considering various factors such as market trends, consumer behavior, and business objectives.
  • Employ existing econometrics models and/or develop new customized models to forecast the most effective marketing investment combination across paid and owned channels, utilizing statistical modeling and machine learning techniques.
  • Lead and mentor a team of analysts to optimize creative elements, audience segmentation strategies, and channel reach and frequency for each campaign, ensuring the highest return on investment.
  • Collaborate with partner teams to design data science products and solutions that cater to diverse groups, optimizing the process of converting queries into reliable answers.
  • Design and analyze controlled experiments and A/B tests to validate model performance, ensuring the accuracy and reliability of our models.
  • Liaise with internal and external partners to ensure the robustness of their models to achieve our specific business outcomes, collaborating with partners to ensure that our models align with our business objectives and yield reliable results.
Requirements
  • Minimum of 12 years of hands-on experience in data science or analytics roles, with a focus on marketing analytics.
  • MS or PhD in mathematics, statistics, computer science, economics, operations research, or a related quantitative field, or equivalent experience.
  • Proficiency in at least one statistical or programming language (e.g., R, Python) for data manipulation and analysis.
  • Expertise in statistical modeling, experimental design, inferential statistics, and machine learning, particularly in marketing mix models, attribution models, and digital analytics.
  • Familiarity with marketing modeling techniques, including ad stock and carryover effects, various transformations to capture diminishing returns effects in linear models, modeling approaches for capturing the combined effects of media investments, creative decay/wear-in/wear-out, and full funnel measurement.
  • Proficient in using data visualization tools (such as Tableau, Shiny, Streamlit) to present data in a visually engaging manner.
  • Strong communication skills and ability to explain complex analytical concepts to individuals without a technical background.
  • Leadership experience, including the ability to lead and mentor a team of analysts.
  • Familiarity with the gaming or entertainment industry is preferred.
  • Proficiency in extracting large datasets from various relational databases using SQL (Amazon Redshift, Oracle, Teradata preferred).
  • Experience in developing and deploying data pipelines and working with data ETL and RESTful APIs.
  • Familiarity with e-commerce analytics topics, including digital tactics, targeting and customer segmentation, lifetime value forecasting, and incremental response modeling.

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