Our Customer:

An established, high-scale advertising technology platform operating in programmatic advertising. Its real-time decision systems directly influence campaign performance, traffic quality and monetization.

Your Tasks:

Design, develop, optimize and maintain machine learning models for large-scale advertising platforms
Build predictive models for CTR, CVR, user behavior, traffic quality and advertising performance
Develop algorithms for bidding, pricing, ad ranking, traffic selection and campaign optimization
Work with high-throughput, latency-sensitive, real-time advertising decision systems
Improve targeting, segmentation and recommendation algorithms
Design A/B tests, offline evaluations and statistical experiments to measure model and business impact
Own ML solutions end to end, from problem definition and feature engineering through deployment, monitoring and iteration
Collaborate with Data Science, ML Engineering, Backend Engineering and Product teams
Improve monetization, advertising efficiency, prediction accuracy and platform performance

Required Experience and Skills:

5+ years of hands-on professional experience in Data Science, Applied ML or algorithm development
Significant practical experience in AdTech, programmatic advertising or real-time advertising platforms
Direct exposure to DSP, SSP, RTB or comparable programmatic advertising environments
Hands-on ML experience in advertising, such as CTR/CVR prediction, bidding optimization, ranking, traffic selection or campaign optimization
Strong command of Python
Strong command of SQL
Hands-on experience working with large-scale datasets
Solid foundations in machine learning and statistical modeling
Deep understanding of feature engineering and model evaluation
Proven ability to design and run experiments to measure model and business impact
Proven experience deploying, maintaining and improving ML models in production
Practical understanding of high-throughput systems, latency constraints and real-time decision-making
Proven ability to take independent technical ownership of ML solutions and deliver measurable business impact
Proven ability to collaborate with cross-functional engineering teams
Strong communication skills for working in an international team
English – Upper-Intermediate

Would Be a Plus:

Experience in AdTech
Experience with DSP bidding systems, SSP optimization, RTB auctions, OpenRTB, auction dynamics or real-time decisioning
Hands-on experience with bid pricing, bid shading or budget pacing
Hands-on experience with traffic quality, inventory selection, yield optimization or monetization
Experience with deep learning, DNNs or embeddings
Experience with recommendation systems or ranking models
Hands-on experience with PyTorch, TensorFlow or scikit-learn
Hands-on experience with Spark, Kafka, Redis, Snowflake or comparable infrastructure
Experience with model serving and latency optimization
Experience with model performance monitoring in production
Solid understanding of advertising KPIs, including CTR, CVR, eCPM, ROI, ROAS, fill rate and revenue yield
Experience with online learning or contextual bandits
Experience with causal inference or advanced experimentation