Senior Applied Scientist II, Ads Optimization

Responsibilities

• Design and evolve real-time bid optimization systems that translate advertiser goals (target ROAS, budget constraints) into optimal auction bids under uncertainty. Formulate the bidding problem as constrained optimization and build the feedback mechanisms that keep bids aligned with realized outcomes.
• Build intelligent budget pacing algorithms that distribute spend across time and auction opportunities. The core challenge: allocating a finite daily budget across stochastic demand while maximizing total value, subject to advertiser constraints and time-varying conversion dynamics.
• Develop the analytical frameworks that connect bidding, pacing, and budgeting into a coherent optimization objective.
• Shape auction mechanics including reserve pricing, multi-slot allocation, and bid-to-price mapping. Reason about mechanism design tradeoffs between advertiser outcomes, platform revenue, and marketplace efficiency.
• Own the full research-to-production loop: diagnose system behavior from large-scale data, formulate hypotheses, design experiments, ship production code, and measure impact. Write technical strategy documents that set the algorithmic direction for the team.

Minimum Qualifications

• MS or PhD in operations research, applied mathematics, control systems, computational economics, or a related quantitative field.
• 8+ years of experience building and deploying optimization or control systems in production environments (not just research prototypes).
• Strong foundation in at least two of: feedback control theory (PID, MPC), convex and stochastic optimization, auction theory and mechanism design, dynamic programming.
• Proficiency in one of the following languages: Go, Java, C++ for production systems and Python for data analysis and offline pipelines.
• Demonstrated ability to translate mathematical formulations into production code that runs at scale (millions of decisions per day, sub-100ms latency constraints).

Preferred Qualifications

• Experience with real-time bidding systems, ad auction optimization, or computational advertising at scale.
• Background in budget-constrained allocation methods. Experience with adaptive control or model-predictive control in production systems.
• Familiarity with causal inference and experimental design for evaluating algorithmic changes in marketplace settings.
• Track record of shaping technical strategy and driving cross-functional alignment between engineering, product, and data science.

Compensation

Base pay ranges by location:

• CA, NY, CT, NJ: $240,000—$253,500 USD
• WA: $230,000—$243,000 USD
• OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI: $221,000—$233,000 USD
• All other states: $201,000—$212,000 USD Eligible for equity grants.