Marketing Data Science Lead

Lead and grow the team. Support, mentor, coach on stakeholder communication, set standards, hire. Make the team better than it is today. What it might look like in practice Representative examples of what you might tackle in your first 6-12 months. Are our attribution and pLTV models shaping investments? Work with Games and marketing on how they actually use attribution and profitability evaluation - in what decisions, with what trust. Close the gap between model and decision quality: what measurement is for, where it stops, what we use at the edges. Are game teams getting what they need from us? Map what game teams actually use and where they want more, reset the cadence and format of how we deliver insight and shape ways we support marketing decisions in games. Pick one game and run a quarter as a deeper partnership. Prove what “good” looks like, then scale. How do we measure what attribution can’t see? How does brand, content, and community feed performance and back? Based on a portfolio of approaches) geo experiments, holdouts, synthetic controls, MMM, lift studies and more) build algorithms for decisions on how we invest in which channels. Deliver answers on how brand, influencer, and community marketing initiatives move downstream player value and translate it into a clear narrative for marketing leadership and games. Where does AI actually move our work? Identify 2-3 places where AI materially changes what we can do - e.g. creative analysis at scale, model-drift diagnostics, decision-support for UA and game teams. Pick one. Ship it. Measure whether it actually changes work. Decide what team owns internally vs. lean on the wider Data and Insights organization. What does “good” look like for a new game on day one? Define measurement and forecasting models before soft-launch - across the mix, not just UA. Productize the approach so it doesn’t get reinvented per game. And many other things. We expect you to take ownership, work independently, and drive the topics you believe matter. To excel here, you Own projects and stakeholders, not just models. You take a business problem and solve it - from game team conversation to shipped outcome. Have deep marketing measurement craft in mobile (or close to it). Built or owned measurement work in mobile gaming, apps or other digital business verticals. Comfortable across the mix - not only performance. Understand F2P games well enough to design best-in-class measurement around them. Are a senior data scientist in practice, not just title. Strong applied stats, causal inference, classical ML, coding. Hands-on with infrastructure and models in production - CI/CD, monitoring, feature stores, large-scale data warehouses, realtime inference. Use AI in your own work. You’ve actually integrated AI tooling into how you do data science. You have a view on where it changes the craft and where it’s noise. Communicate clearly across audiences. Able to explain a model to a game lead in three minutes. Or a methodology to a senior DS in thirty. No 100-page decks. Comfortable saying I don’t know and we shouldn’t do that because. Lead and grow people. Mentored or managed data analysts/data scientists. Have views on standards, best practices, when to step in, when to step back. Care about games and the players. You bring a player-centric lens even when the work is deep in LLM models business applications. Operate well in autonomy and ambiguity. Don’t need a pre-set structure to be productive. Comfortable being wrong, learning fast, changing direction and impact over credit.