Bring in your strong scientific experience in researching, deploying and architecting machine learning (ML) and deep learning (DL) based solutions in areas such as recommendations to help our millions of customers in their inspiration journey. Work across teams to create a state-of-the art science stack that enables the product strategy and be a driver of ML architecture and design principles.
Develop scientific roadmaps to tackle challenging fashion problems with state-of-the-art solutions and to keep us innovative. Contribute to our scientific efforts with rigorous peer reviews and through publications in international conferences.
Mentor junior scientists and actively collaborate with other teams and share knowledge and expertise within our talented applied science community.
Work closely with applied science leaders, software engineers, product managers and other business stakeholders to bring our state-of-the-art solutions to the customers and to discover and identify new opportunities.
A solid background (MSc or PhD degree) in a quantitative scientific discipline with a strong understanding of machine learning as well as end-to-end development lifecycle of data-driven products.
Proven academic or industrial track record in Machine Learning, Deep Learning, Recommender Systems, Computer Vision and Image Understanding, Probabilistic Generative Modelling and/or Natural Language Processing.
Solid software engineering skills, expert in Python (and relevant packages like numpy, scipy, pandas), experience in Cloud Computing services (e.g. AWS), Spark or PySpark
Proven industry experience with a state of the art deep learning framework such as PyTorch or TensorFlow
In-depth understanding of machine learning theory in one area, plus a broad overview of the other areas, ML methods in general, and knowledge of current ML literature
Experience developing long term research roadmaps and applying the scientific method to tackle research problems. Strong drive to partnering with product and science/engineering managers to connect and solve business problems with scientific solutions.
You have effective communication skills in English, with an ability to understand and translate business and user requirements across teams.
You are solution oriented, adaptable to change, and have a drive for continuous innovation.
Experience and competence in proactively working through uncertainty to bring clarity.
ADDITIONAL PREFERRED SKILLS
Experience with highly distributed, scalable and resilient software development with an understanding of an overlap between software engineering and applied science.
Solid track record of publications in major machine learning, deep learning and data science conferences and journals.
Experience working in cross-functional engineering and science teams within an international, agile environment