Marketing Data Analyst

SLAY, founded in December 2022, has swiftly made its mark in the mobile world. In 2023, we developed several apps and games that reached the #1 spot in the App Store across 3 continents.

Backed by some of the world's most esteemed investors, including Accel, Kevin Weil, Riccardo Zacconi Scott Belsky, and Ilkka Paananen, SLAY is rapidly growing and reaching millions of users.

Our mission is to create digital life through AI and become the leading company for virtual friends that live, grow and evolve like humans—starting with Pengu. Pengu has become the biggest AI character app with its own IP in the US and Europe.

Tasks

• Data Analysis and Insights: Enable tactical and strategic decision-making by providing insightful analyses, developing predictive models, and creating dashboards.

• Predictive Analytics: Build and interpret predictive models to forecast Lifetime Value (LTV) and user behavior, optimizing marketing spend and strategies.

• Attribution Modeling: Implement and refine attribution models—including probabilistic and multi-touch models—working with SKAdNetwork (SKAN) data and other privacy-centric attribution methods.

• A/B Testing: Design, analyze, and interpret the results of A/B tests to inform marketing strategies and improve campaign performance.

• Cross-functional Collaboration: Work closely with marketing, user acquisition, and data engineering teams to understand data needs and deliver solutions that drive business objectives.

Requirements

Experience:

• 3+ years in a role such as Data Analyst or Data Scientist with a strong emphasis on marketing analytics.
• Currently employed in a gaming company, actively addressing similar challenges is a plus.

Technical Proficiency:

• Strong expertise in SQL and experience with data analysis tools.
• Proficiency in a programming language (e.g., Python or R).
• Experience with cloud data warehouses is a plus.

Predictive Analytics:

• Experience with predictive modeling techniques and forecasting LTV.
• Familiarity with machine learning concepts and algorithms relevant to marketing analytics.

Attribution Modeling:

• Strong knowledge of attribution models, including probabilistic and multi-touch models.
• Familiarity with mobile attribution challenges like SKAdNetwork (SKAN).

Data Visualization:

• Proficiency with visualization tools like Tableau, Looker, or Power BI.

Marketing Knowledge:

• Deep understanding of performance marketing, user acquisition strategies, and performance metrics (e.g., CPI, CPA, ROAS, K-factor).

Analytical Mindset:

• Strong problem-solving skills and a data-driven approach.
• Ability to handle complex datasets and extract actionable insights.

Communication Skills:

• Excellent verbal and written communication skills.
• Ability to convey complex data insights to non-technical stakeholders.

Adaptability:

• Proven ability to deliver high-quality results in a fast-paced, high-pressure environment.