Principal Data Scientist

What you'll do

• Act as a top-level expert and thought leader in data science, responsible for strategic direction of modeling and experimentation
• Tackle problems of highest complexity and impact, inventing or extending current approaches
• Define methodologies, standards, and frameworks adopted company-wide
• Bridge technical depth with business acumen to influence product strategy and company decisions
• Exercise expert judgment in selecting and creating analytical approaches in ambiguous areas
• Operate with substantial independence, reviewed by long-term impact and business alignment
• Serve as trusted advisor to senior leaders across Product, Engineering, and business
• Mentor Senior and Lead Data Scientists, review critical work, set standards for rigor and impact
• Communicate complex statistical and ML concepts clearly to engineers and executives
• Define long-term modeling strategy for user lifetime value prediction, recommendation, real-time bidding, fraud detection, and creative optimization
• Lead design and development of novel ML and statistical approaches including probabilistic graphical models, Bayesian methods, deep learning, and advanced decisioning systems
• Architect end-to-end data science solutions from problem framing to deployment and monitoring
• Establish and evolve experimentation and causal inference frameworks (A/B testing, multi-armed bandits, uplift modeling)
• Drive high-visibility, cross-functional initiatives leveraging data science to unlock new product capabilities or revenue streams
• Set best practices and standards for modeling, code quality, documentation, reproducibility, and ethical AI
• Identify and prioritize new data sources and signals to improve model performance or unlock new use cases
• Represent company’s data science capabilities externally as needed
• Own systems/products across full adtech stack: bidding infrastructure, yield management, audience data, and measurement

Requirements

• 12+ years of related experience
• Deep expertise in machine learning and statistics including forecasting, large-scale recommendation systems, information retrieval, probabilistic graphical models, Bayesian inference, causal inference, and deep learning
• Experience developing and deploying production machine learning systems at scale, including batch and real-time/online models
• Strong software engineering skills with experience building production-quality ML code and pipelines (version control, testing, CI/CD)
• Proficiency in Python or similar for data science and strong SQL skills
• Experience with major ML and numerical libraries such as PyTorch, TensorFlow, JAX
• Solid understanding of big data ecosystems like Spark, Hadoop, Databricks, BigQuery
• Experience working in modern cloud environments (AWS, GCP, or Azure)
• Proven track record of driving significant business impact through data science initiatives
• Strong leadership, collaboration, and influence skills
• Ability to synthesize complex technical and business information into clear recommendations

Tech stack