Senior Principal Applied Scientist - Ranking (all genders)

Description

The Ranking team at the company helps customers find what they are looking for while they browse fashion. The team is building the next generation of its discovery systems using deep learning and multi-objective optimization, creating and tuning ranking algorithms that make the customer experience more personal and engaging.

As a Senior Principal Applied Scientist, you will tackle hard fashion problems with new methods and help set the scientific roadmap. You will work with a group of experienced scientists and engineers to build models and machine learning products used by more than 53 million customers.

Responsibilities

• Apply deep learning and multi-objective optimization to research, deploy and design machine learning systems that improve how millions of customers move from browsing to buying through product selection and ranking.
• Work with several teams to build a state-of-the-art science stack that fits the product strategy, and set architectural and design principles for it.
• Write long-term research roadmaps that cover several product areas and tie to concrete business goals.
• As part of the leadership team, act as a sounding board and trusted advisor to senior leadership. Advise on hiring, strategic direction, and whether to build or buy.
• Mentor senior and principal scientists and share your knowledge across the company's science community.
• Partner closely with applied science leaders and product managers to deliver machine learning solutions and find new growth opportunities. This includes leading large, high-uncertainty projects into production, applying scientific methods to complex problems, and aligning with other teams, stakeholders and senior leadership.
• Build, test, deploy and research ML models for personalization and ranking across the full customer journey.

Requirements

• A PhD in machine learning and AI, or equivalent experience, plus 8+ years of industry experience. Experience with deep learning, multi-armed bandits or large-scale sequential decision-making is important, ideally applied to personalization or ranking.
• Experience leading the scientific roadmap for teams whose products span several areas, and working with senior applied science leaders.
• Ability to find new business opportunities with product, science and engineering managers and turn business problems into scientific solutions.
• An extensive record of solving real-world problems with machine learning across the full product cycle, and of raising the bar for methods, algorithms and technology, including architecture, design patterns and research roadmaps.
• Recognised authority in deep learning with a strong record of turning research into state-of-the-art results, including solid publications.
• Extensive hands-on deep learning experience, including Python and frameworks such as PyTorch or TensorFlow for reinforcement learning. Cloud computing experience with AWS is required.

Conditions

The company is Europe's leading online fashion platform, operating in 23 markets and serving more than 52 million active customers. It works across fashion, logistics, advertising and research.

Hiring is based on qualifications, merit and business need. Diverse backgrounds are welcome.

Benefits

• A culture built on trust, empowerment and constructive feedback, with open source work, meetups, game nights, more than 70 internal technical and social guilds, tech talks, an internal tech academy, blogs, product demos, and parties and events.
• Competitive salary, an employee share shop, a 40% discount on the company shopping, partner discounts, centrally located offices, public transport discounts, municipal services, good IT equipment, flexible working hours, extra holidays, paid volunteering time, free drinks and fruit, and a range of sports and health options.
• Extensive onboarding, mentoring, personal development and an international team of experts.
• Relocation help for international hires, family services, and parent and child rooms in selected locations.