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.
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