ph3About the Company /h3 pPlayvalve is a vibrant fast-growing gaming studio with an eye on the future. Our mission is to build fun and relaxing games meant to last, and we do that by bringing together a team of experts who love what they do. Our talented designers, artists, engineers, marketeers and analysts have created several games delivering joy and entertainment to over 20M users across more than 12 languages and 90 countries. /p h3About the Role /h3 pAs a bPrincipal Applied Scientist /b, you’ll bridge rigorous science with production impact. This is not a pure engineering role, we need someone who bthinks like a scientist first /b: formulating hypotheses, designing experiments with statistical rigor, and bbuilding models grounded in solid mathematical foundations /b. You’ll then work with Engineering to productionize these solutions at scale. /p pYou’ll bown end-to-end problems in user behavior modeling, ad monetization optimization, and real-time decisioning systems /b. This means deep work on causal inference, probabilistic modeling, experimentation design, and optimization, not just applying off-the-shelf ML libraries. You’ll need to understand the ’why’ behind the models, not just the ’how.’ /p pWe’re looking for someone who bholds an advanced degree in a quantitative field /b, and has bbattle-tested their science in production environments /b. You should be equally comfortable working through the math behind a model as you are debugging production pipelines. /p h3Key Responsibilities /h3 h3Scientific Leadership Problem Framing (50%) /h3 ul liFrame ambiguous business problems as well-defined scientific questions with testable hypotheses. /li liDesign and analyze controlled experiments (A/B tests, multi-armed bandits) with proper statistical rigor. /li liBuild probabilistic models for user behavior, lifetime value prediction, and ad response. /li liApply causal inference techniques to untangle correlation from causation in observational data.



/li liStay current with research literature and bring cutting‑edge methods to production (when appropriate). /li /ul h3Production ML Systems (20%) /h3 ul liPartner with Engineering to build real‑time decisioning APIs for game configuration and ad serving. /li liImplement proper monitoring, evaluation frameworks, and fallback mechanisms. /li liShip models as stable services with clear performance guarantees. /li /ul h3Experimentation Measurement (20%) /h3 ul liDefine success metrics and design experiments that provide unambiguous answers. /li liBuild experimentation infrastructure and champion rigorous hypothesis testing across the org. /li liQuantify uncertainty and communicate statistical trade‑offs to non‑technical stakeholders. /li liTranslate experimental results into business decisions with estimated impact. /li /ul h3Strategic Collaboration Mentorship (10%) /h3 ul liWork with Product, Marketing, and Ad Monetization to prioritize high‑impact projects. /li liMentor other data scientists on scientific rigor. /li liEstablish standards for model evaluation, code quality, and documentation. /li liCommunicate complex technical concepts clearly to diverse audiences. /li /ul h3Requirements /h3 ul libPhD or MSc in Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, or closely related quantitative field. /b /li lib7+ years in data science with 2+ years owning production ML systems. /b /li liExpert‑level Python and SQL; comfortable writing production‑quality code. /li liDeep understanding of probability theory, statistical inference, and mathematical optimization.



/li liStrong grasp of experimentation design and statistical hypothesis testing (power analysis, multiple testing corrections, variance reduction techniques). /li liDemonstrated ability to read, critique, and apply research papers to real‑world problems. /li /ul h3Nice to have /h3 ul liExperience with modern ML frameworks (TensorFlow, PyTorch, JAX, or similar) for deep learning and/or probabilistic programming (e.g., PyMC, Stan, TensorFlow Probability). /li liBuilt and deployed end‑to‑end ML systems that handle millions of requests/predictions. /li liProficiency with cloud platforms (GCP, AWS, Azure) for data processing and model serving. /li liPublished research in top‑tier venues (NeurIPS, ICML, KDD, JMLR, etc.) or industry conferences. /li liRelevant Domain Experience (1‑2 of the following strongly preferred): ul liGaming/mobile apps: LTV prediction, retention modeling, monetization optimization /li liAd tech: Bidding strategies, auction mechanisms, ad serving optimization /li liMarketplace/platforms: Two‑sided market dynamics, pricing, recommendations. /li liSubscription/SaaS: Churn prediction, upsell optimization, cohort analysis /li /ul /li /ul h3What we offer /h3 ul libThe Role: /b Own data science solutions from scratch with full autonomy. Join a meeting‑less company, no status updates, no sync calls, just deep work and results. /li libLocation Flexibility: /b Based in Barcelona (office with terrace, BBQ, beer tap). Flexibility policy: the data team comes in regularly because they want to, not because they have to. /li libCompensation Perks: /b Competitive compensation based on experience, phantom stocks, relocation package, health insurance, ticket restaurant, unlimited vacations, regular company off‑sites. /li libThe Challenge: /b 15 games in market + ambitious pipeline. Industry inflection point with AI and automation. Profit‑backed studio with no VC pressure and resources to do it right. /li /ul /p #J-18808-Ljbffr

📌 Principal Applied Scientist (Barcelona)
🏢 Bluetile
📍 Barcelona