Our Customer:
A fast-growing product company operating in the digital advertising space, focused on building large-scale, data-driven platforms for real-time decision-making. The company develops intelligent systems that optimize campaign performance, user targeting, and revenue generation in highly dynamic environments with massive data volumes.
Your tasks:
- Design and enhance machine learning models that drive real-time bidding and campaign optimization
- Analyze large-scale datasets to uncover inefficiencies and identify growth opportunities
- Formulate hypotheses, run experiments, and interpret results to improve system performance
- Improve targeting strategies to maximize conversion rates and traffic quality
- Develop approaches for measuring true campaign impact beyond standard attribution models
- Optimize resource allocation strategies to balance exploration and performance
- Detect anomalies and improve traffic quality through data-driven techniques
- Collaborate closely with cross-functional teams to deliver scalable ML solutions
Required experience and skills:
- 5+ years of experience in Data Science or Machine Learning in high-scale environments
- Experience in the AdTech industry is strictly required
- Strong programming skills in Python and solid experience with SQL
- Hands-on experience with large datasets and distributed data processing tools (e.g., Spark or similar)
- Practical experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn
- Strong knowledge of statistics, including experimental design and causal analysis
- Proven experience in running and analyzing experiments (A/B testing or similar)
- Ability to understand complex systems and optimize them holistically
- Strong communication skills and ability to work with both technical and non-technical stakeholders
Would be a plus:
- Experience with auction-based or dynamic pricing systems
- Familiarity with large-scale experimentation platforms
- Understanding of budget optimization and traffic allocation strategies
Working conditions
5-day working week, 8-hour working day;
Remote work.