Data Scientist

Job Description

FARFETCH exists for the love of fashion. Our mission is to be the global platform for luxury fashion, connecting creators, curators, and consumers.We're a positive platform for good, bringing together an incredible creative community made up by our people, our partners and our customers. This community is at the heart of our business success. We welcome differences, empower individuality and celebrate diverse skills and perspectives, creating an inclusive environment for everyone. We are FARFETCH for All.DATAWe're a Data team that does it all: big data engineering, machine learning, and deep-dive analytics and insight. We're a diverse, global team that creates Data solutions to provide an unrivaled customer experience. Whether it's churning gigabytes of e-commerce data, using AI to recommend the latest trends, or understanding our customers better than anyone else, we use data to promote FARFETCH's growth.PORTOOur Porto office is located in Portugal's vibrant second city, known for its history and its creative yet cosy environment. You can have an informal meeting in the treehouse or play the piano during your lunch break!THE ROLEReporting to the Data Science Manager you will join Farfetch being part of building the next-generation intelligent platform for online luxury fashion, powered by large-scale data and Machine Learning, Deep Learning, Information Retrieval and Natural Language Processing algorithms. You will join a team of Data Scientists and Engineers working on the ranking ecosystem which serves, daily, millions of optimized listing pages to customers everywhere in the world. You will help provide intelligent algorithms, at scale, while working with product, business and tech to meet goals.

WHAT YOU´LL DO

• Develop machine learning models alongside conventional algorithms for ranking on a world leading luxury fashion platform.
• Conduct practical research developing new models with a scientific mindset and a determined focus on delivery.
• Work with the engineering team to integrate machine learning based algorithms into the platform.
• Contribute ideas and proposals for future initiatives to improve the ranking ecosystem.
• Scientific and technical publications are possible and encouraged.

WHO YOU ARE

• A professional with 2 years' experience in a Data Science related role, working within a Retail and E-commerce environment;
• A professional with an understanding of Information Retrieval, Learning to Rank (e.g., RankNet, ListNet, lambdaMART), algorithm performance metrics (e.g., NDCG);
• Fluent in Python, common numerical and Math & ML packages (NumPy, SciPy, scikit-learn, pandas, Keras, TensorFlow, and PyTorch);
• Experienced in dealing with large amounts of data and building data pipelines;
• Non-relational databases (e.g., Cassandra, Big Query) and streaming platforms know-how (e.g., Kafka);
• A professional with English skills, both written and spoken;
• Interested to keep up-to-date with scientific advancements.

REWARDS & BENEFITS

• Health insurance for the whole family, flexible working environment and well-being support and tools
• Extra days off, sabbatical program and days for you to give back for the community
• Training opportunities and free access to Udemy
• Flexible benefits program
• FARFETCH Equity plan

EQUAL OPPORTUNITIES STATEMENT

• FARFETCH is an equal opportunities employer ensuring that all applicants are treated equally and fairly throughout our recruitment process. We are determined that no applicant experiences discrimination on the basis of sex, race, ethnicity, religion or belief, disability, age, gender identity, ancestry, sexual orientation, veteran status, marriage and civil partnership, pregnancy and maternity, or any other basis prohibited by applicable law. We continue to build our consciously inclusive culture as part of our Positively FARFETCH strategy throughout our business, partnerships and communities.

Work with a team of Data Scientists, Engineers, and Product Designers to help build and increase our data-driven products, through research and experimentation in Information Retrieval.