Overview As an Applied Scientist in the Partner Science team, you will build ML/AI and causal inference solutions that help advertisers grow through partner networks. You will design and productionize models, run experiments, and translate findings into business impact across cross-functional partners. You'll operate with autonomy on ambiguous, high-impact problems and collaborate with product, engineering, and sales stakeholders to scale science-driven interventions. This role offers the chance to apply ML, causal analysis, and Gen-AI techniques to optimize the partner flywheel and advertiser experience.
Compensation / Benefits- health insurance (medical, dental, vision)
- 401(k) matching
- paid time off
- RSUs
- sign-on payments
- parental leave
Responsibilities- Design, prototype, validate, and productionize ML models across domains including predictive, causal, and text analytics/LLMs
- Own production science models end-to-end from scoping to deployment and ongoing refinement
- Design and run scalable A/B experiments to validate interventions and features
- Perform hands-on data analysis on large-scale advertising data using centralized knowledge bases and data infrastructure
- Collaborate with MLOps to deploy models and with Data Engineering to curate datasets
- Translate model outputs into business impact for cross-functional stakeholders and drive alignment
- Contribute to model quality monitoring, data quality frameworks, and operational excellence
- Mentor teammates and foster a culture of learning and knowledge sharing
Key requirements- 3+ years of building models for business applications
- PhD, or Master's degree and 4+ years in CS, CE, ML or related field
- Programming experience in Java, C++, Python or related language
- Experience with algorithms, data structures, numerical optimization, data mining, parallel/distributed computing, or high-performance computing
- 3+ years of hands-on predictive modeling and large data analysis experience
- collaboration with cross-functional teams
- ability to communicate technical concepts to non-technical stakeholders
- mentoring peers and contributing to a culture of learning
- ML/AI model development
- causal inference and A/B testing
- text analytics/LLMs and Gen-AI applications
