Senior Data Scientist - Ads Recommendation - Contractor

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 organization 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 monetization 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. By June 2023, Petal Ads had cooperated with advertisers spanning 200+ industries, while more than 60,000 apps worldwide had integrated Ads Kit.

About the Job:

As a senior data scientist, you will join a team of talented experts to apply your deep knowledge of machine learning, mathematics, statistics and modelling skills to the real-life production dataset of Huawei Ads. Especially, by leveraging your previous industrial and academic domain experiences, you will bring your unique value to the team and be able to influence across the global teams.

We are looking for a highly skilled and experienced computational advertising domain expert passionate about working on problems at the intersection of academic research and applied industrial practice. You will work on some trending/cutting-edge projects using the latest and most advanced technologies available in the domain and Huawei to help the Ads business grow and expand globally.

Our team works closely with the recommendation and cloud service group to deliver the most advanced models and best features. You will also communicate frequently with Huawei AALA and HQ experts to benefit from each other's findings.

Responsibility:

• Conduct in-depth research and exploration and develop/deploy innovative advertising algorithms tailored to specific scenes and business applications, such as pCTR/pCVR optimization, inventory allocation, and AI-assisted ads content optimization, using state-of-the-art methodologies.
• Identify ways to improve ad data/product reliability, efficiency, and quality.
• Engage regular A/B tests and statistical analysis to conclude the impact of proposed models.
• Monitor, analyse, and tune the performance of measurement metrics specific to each targeted ad application domain.
• Collaborate closely with cross-functional teams including field experts, product owners, and internal stakeholders to inform, influence, support, and execute product strategy and R&D decisions.
• Master's/PhD degree in a quantitative discipline (e.g., Computer Science, Information Systems, Mathematics, Statistics and Physics).
• 4 years + of proven experience in delivering machine learning products. Knowledge and working experience with recommender systems/reinforcement learning/data governance/privacy are preferred.
• In-depth understanding and experience in data analytics or data science with rich experience (2 years +) working with advertising-related data or in the computational advertising field.
• Solid understanding and expertise in developing machine learning models with Python and TensorFlow.
• Strong analytic skills in working with structured/unstructured datasets.
• Experience with SQL or HIVE SQL, understanding the basic principles and tuning strategies of Hive/MySQL.
• Excellent communication skills to elaborate ideas clearly and concisely in writing and orally to individuals and groups.

Requirements:

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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