About the RoleWe're looking for an Applied Scientist who solves hard mathematical problems in marketing attribution through both algorithmic innovation and production-quality implementation. You'll design novel approaches to measurement challenges, implement them as production systems, and work directly with customers to ensure statistical rigor at enterprise scale.
This role is ideal for someone who wants to apply deep technical expertise to real-world problems-shipping code that makes a difference, not just publishing papers.
What You'll Do- Design and implement novel approaches to marketing measurement problems, shipping working code
- Build production systems for causal inference that maintain statistical rigor at enterprise scale
- Develop algorithms that are both mathematically sound and computationally efficient
- Collaborate with customers to understand their measurement challenges and develop technical solutions
- Create tools and libraries that enable both internal teams and customers to leverage advanced analytics
- Document research and implementation decisions for reproducibility and knowledge transfer
What Will Help You SucceedApplied Science & Engineering- 5+ years developing and shipping research code in production environments
- Strong mathematical background - statistics, probability, optimization, causal inference
- Proficient Python developer - can write production-quality code, not just notebooks
- Causal inference expertise - practical experience applying causal methods to real problems
- Data-intensive systems - experience processing and analyzing large datasets
- Research to production - track record of turning research ideas into shipping features
- Communication skills - can explain complex technical concepts to varied audiences
Domain & Advanced Skills- MS or PhD with significant applied research experience
- Background in econometrics, statistics, or computational social science
- Experience in marketing analytics, A/B testing, or measurement domains
- Understanding of ML engineering and MLOps practices
- Ability to work directly with customers on technical problems
- Experience with both Bayesian and frequentist statistical methods
Nice to Haves- Published applied research or technical writing
- Experience in consulting or customer-facing technical roles
- Background in operations research or decision sciences
- Familiarity with GPU computing and performance optimization
- Understanding of privacy-preserving analytics and differential privacy
