Senior Data Scientist (AdTech / DSP Systems)

About the role

We are looking for a Senior Data Scientist to lead the evolution of machine learning systems within a high-scale AdTech ecosystem.

This is not a “campaign optimization” role. You will work on the core decision-making systems (DSP level) — where pricing, bidding, pacing, and experimentation interact in real time across billions of events per day.

You will be shaping the “brain” of the platform: a set of interconnected systems where small improvements can have a large business impact.

What you’ll work on

• Pricing & Budget Scalability
  Scale budgets while maintaining performance and controlling overspend
• User Selection Optimization
  Improve CVR through smarter targeting and traffic selection
• Exploration Efficiency
  Reduce wasted spend while preserving learning
• Incrementality Measurement
  Measure true lift vs attributed performance
• Fraud Detection & Filtering
  Improve traffic quality and filtering strategies

What we expect from you

System-level thinking

• Ability to reason about interacting systems, not isolated models
• Understanding of:
  • auction dynamics
  • bidding vs pacing trade-offs
  • feedback loops between models
• Seeing problems beyond segmentation or heuristics

Strong experimentation mindset

• Designing experiments, not just running A/B tests
• Experience with:
  • metric design (proxy vs north star)
  • online vs offline evaluation gaps
  • interference and auction effects
• Ability to translate system changes into measurable impact

Ownership & impact

• Proven track record of driving business impact, not only building models
• Ability to explain:
  • expected vs actual impact
  • why results differ
• Performance accountability mindset

AdTech domain understanding

• Solid understanding of:
  • targeting vs bidding vs supply dynamics
• Experience working close to DSP / auctions/pricing systems

Collaboration maturity

• Ability to work across DS, MLE, and Product
• Understanding trade-offs and constraints
• Experience making decisions in ambiguous environments

Core responsibilities

• Turn ambiguous problems into testable hypotheses
• Design and run offline analysis + online experiments
• Use intermediate metrics to understand system behavior
• Build prototypes and iterate quickly
• Identify where system-level improvements are required
• Collaborate across teams to drive end-to-end impact

Requirements

• 5+ years in Data Science / Machine Learning
• Strong experience in high-frequency systems (AdTech / FinTech / similar)
• Python & SQL proficiency
• Experience with big data tools (Spark, Snowflake, etc.)
• Strong ML background (TensorFlow, PyTorch, or similar)
• Solid foundation in:
  • probability
  • statistics (frequentist / Bayesian)
  • causal inference
• Master’s or PhD in a quantitative field

Nice to have

• Experience with DSP / auctions / pricing systems
• Experience with large-scale experimentation