Our Customer:
Our customer is a technology company developing data-driven solutions for digital advertising and real-time campaign optimization. The team works with large-scale data and Machine Learning to support automated advertising decisions and improve campaign performance.
Your tasks:
- Develop, improve, and operate ML models used in production advertising systems.
- Work on ML problems related to bidding, bid optimization, and real-time advertising decision-making.
- Develop models for CTR/CVR prediction.
- Optimize campaign performance, budget allocation, and pacing.
- Work on ranking and decisioning models for advertising systems.
- Analyze large-scale advertising data and identify opportunities for optimization.
- Translate business and advertising problems into practical Data Science / ML solutions.
- Evaluate model performance and business impact using relevant advertising KPIs.
- Conduct experiments and A/B testing to validate and improve ML solutions.
- Collaborate with engineering and product teams to deploy and continuously improve production models.
Required experience and skills:
- 5+ years of commercial experience in Data Science / Applied Machine Learning.
- 3+ years of hands-on experience in AdTech / Programmatic Advertising.
- 2+ years of direct experience with RTB / real-time bidding systems.
- Commercial experience applying ML/Data Science to advertising optimization.
- Hands-on experience with bidding or bid optimization.
- Experience with CTR/CVR prediction, campaign optimization, ranking, pacing, budget allocation, or advertising decisioning.
- Experience developing, improving, or operating ML models in production.
- Strong Python skills.
- Strong SQL skills and experience working with large-scale datasets.
- Experience translating business problems into ML/Data Science solutions.
- Strong analytical and problem-solving skills.
- Strong English communication skills.
Would be a plus:
- Experience with DSP platforms.
- Experience with bidder logic, bidding algorithms, or bid-price optimization.
- Knowledge of OpenRTB and auction dynamics.
- Experience with real-time decisioning or pre-bid optimization/filtering.
- Experience with user acquisition, retargeting, or performance advertising.
- Experience with large-scale real-time ML.
- Experience with online experimentation / A/B testing.
- Experience with contextual bandits, online learning, or reinforcement learning.
Working conditions
5-day working week, 8-hour working day;
Remote work.