Senior data scientist — ai for mobile gaming

Senior Data Scientist — AI for Mobile GamingAbout the Role We’re looking for a world-class Senior Data Scientist specializing in Applied AI for Mobile Gaming to push the boundaries of how user acquisition, creative production, and gameplay insights are powered by next-gen models. You’ll work across large-scale marketing datasets, gameplay telemetry, and creative assets to architect intelligent systems that help mobile games grow faster and smarter. This is a highly technical, high-impact role—not just building models, but designing the next evolution of voice-over automation, text-to-video generation, LLM interpretability, and video content deconstruction pipelines . You’ll partner closely with product, engineering, and creative teams to take breakthrough research and convert it into production-grade systems used across millions of players and thousands of creatives. If you love cutting-edge AI, thrive in ambiguous problem spaces, and want to shape the future of mobile gaming growth, this role is for you. What You’ll Own End-to-end model development for marketing, gameplay, and creative pipelines—spanning data ingestion, modeling, deployment, and ongoing optimization. Advancement of Voice-Over AI technologies including neural TTS, style transfer, emotion modeling, and production-ready audio synthesis. Innovation in text-to-video systems , designing pipelines that combine LLM prompting, diffusion-based video generation, animation workflows, and creative constraints for mobile ads. Research & development on LLM decoding and interpretability , enabling better model alignment, controllability, and explainability for creative and marketing use cases. Deconstruction of video assets for automated tagging, scene breakdown, motion analysis, attention mapping, and actionable creative insights. Production-scale data science architecture , enabling real-time scoring, predictive performance modeling, and creative optimization for mobile UA campaigns. Cross-functional leadership , translating complex data science breakthroughs into creative, product, and business roadmap impact. Continuous exploration of state-of-the-art AI , evaluating new model architectures, agent systems, and multimodal research—and rapidly prototyping what’s most promising. What You Bring 5–8+ years of experience in data science, applied machine learning, or AI research , ideally within gaming, ad tech, or consumer-scale products. Expert-level proficiency in LLMs, multimodal models, transformers, diffusion models , and modern AI toolchains. A deep understanding of ML systems design , including distributed training, model serving, feature engineering at scale, and GPU/accelerator optimization. Strong experience with large datasets , including marketing data, creative performance data, or telemetry from mobile games. Proficiency in Python, Py Torch, Tensor Flow, JAX , and modern vector databases and embeddings frameworks. Experience shipping ML models to production environments with real-world performance constraints. A proven ability to translate ambiguous business needs into concrete, scalable technical solutions. What Will Make You Great at This Role You think like a systems architect , not just a model builder—seeing how LLMs, audio, video, and game data connect to create end-to-end value. You are obsessed with the creative side of gaming , and have intuition for what makes mobile ads perform. You have hands-on experience with video segmentation, multimodal embeddings, audio analysis, or diffusion-based video generation . You are passionate about LLM interpretability , decoding strategies, token-level insights, and controllability techniques. You’re energized by greenfield challenges and can move fast from concept → prototype → production with minimal guidance. You stay relentlessly current on new AI research and can evaluate emerging models with a strong sense for what will materially impact mobile gaming. You balance deep technical excellence with pragmatism—knowing when to build from scratch, when to leverage open-source models, and when to adapt cutting-edge research.