Location: Dublin, Ireland

About Huawei

Huawei is a leading global information and communications technology (ICT) infrastructure and smart devices provider. With integrated solutions across four key domains – telecom networks, IT, smart devices, and cloud services – we are committed to bringing digital to every person, home, and organisation for a fully connected, intelligent world.

About Huawei Petal Ads

Petal Ads is a smart marketing platform for Huawei devices. It provides marketing and traffic monetisation services for advertisers and publishers worldwide to help them achieve business growth and improve brand value. It does this by leveraging Huawei's "1+8+N" all-scenario ecosystem, Huawei apps' marketing capabilities, massive premium third-party traffic, and powerful ad technologies. Petal Ads has a global user base of more than 730 million monthly active users covering 220+ Global markets, while more than 60,000 apps worldwide have integrated Ads Kit. In addition, 20+ Huawei apps and 10+ mainstream ad formats are at users' disposal.

About the Job:

As a Senior Data Scientist in Huawei Ads, you will join a world-class team of experts to apply advanced machine learning, statistics, and mathematical modelling to one of the largest-scale advertising and recommendation platforms. You will leverage your strong industrial and academic background to design and deploy cutting-edge algorithms that directly impact Huawei’s global advertising ecosystem.

We are seeking a highly skilled and experienced domain expert who is passionate about recommender systems and computational advertising. You will tackle challenging problems at the intersection of academic research and applied industrial practice—ranging from personalisation, ranking, and reinforcement learning to large-scale model deployment. Your work will help shape the future of user and advertiser experience within Huawei Ads and accelerate global business growth.

Our team collaborates closely with Huawei’s recommendation, search, and cloud service groups, as well as AALA and HQ experts. This role provides you with opportunities to validate ideas quickly, influence cross-functional strategies, and contribute to both production innovation and cutting-edge research.

Responsibility:

• Conduct research, development, and deployment of innovative recommendation and advertising algorithms, including but not limited to:
• Personalised ranking models (pCTR/pCVR prediction, multi-task learning, representation learning).
• Ads and content recommendation (user-ad matching, contextual targeting, multi-modal signals).
• Sequential and session-based recommendation (deep learning, transformers, graph-based models).
• Fairness, diversity, and long-term value optimisation in recommendation.
• Drive improvements in ad data reliability, efficiency, and quality by leveraging large-scale recommendation datasets (structured, unstructured, and multi-modal).
• Design and evaluate A/B tests and statistical experiments to measure the effectiveness of algorithms and new recommendation features.
• Monitor, analyse, and tune model performance across core recommendation and advertising KPIs (RPM, eCPM, engagement, retention, relevance).
• Collaborate with product managers, engineers, and domain experts to translate business needs into scalable, recommendation-driven solutions.
• Stay ahead of emerging trends in recommender systems (e.g., self-supervised learning, foundation models for recsys, retrieval-augmented models, federated learning for privacy-preserving recommendations).

Requirements:

• Master’s or PhD in a quantitative discipline (Computer Science, Information Systems, Mathematics, Statistics, Physics, or related).
• 4+ years of proven experience in delivering large-scale machine learning products, with a focus on recommender systems, personalisation, or ranking.
• Strong expertise in machine learning for recommendation: collaborative filtering, matrix factorisation, deep learning (DNNs, transformers, graph neural networks), reinforcement learning, or bandit algorithms.
• Hands-on experience with recommender system frameworks and libraries (e.g., TensorFlow Recommenders, PyTorch).
• Solid programming skills in Python and experience in production ML pipelines.
• Strong analytic and experimental design skills for A/B testing and offline evaluation of recommendation models.
• Proficiency in SQL or Hive SQL with an understanding of distributed data processing and query optimisation.
• Excellent written and oral communication skills for presenting complex recommendation solutions to both technical and non-technical stakeholders.
• (Preferred) 2+ years of direct experience in computational advertising, recommender systems in large-scale platforms, or working with advertising/recommendation datasets.

Check out Life at Huawei Ireland Research Centre: https://www.youtube.com/watch?v=3gR64sYSnOA&feature=youtu.be

ONLY CANDIDATES WHO MAY LIVE AND WORK IN IRELAND WITHOUT RESTRICTION CAN BE CONSIDERED FOR THIS POSITION.

DUE TO THE HIGH VOLUME OF REPLIES, ONLY CANDIDATES WHO ARE SHORTLISTED FOR INTERVIEW WILL BE CONTACTED.

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