Head of Data Science
We are looking for someone who wants to help set the standard in data science for operations.
We solve complex problems with carefully defined approaches backed by data, and get to see the outcome of the implemented solutions impacting hundreds of employees and millions of customers.
In particular, Operations in Farfetch includes our global Supply Chain with millions of orders and thousands of routes, and our product imagery and descriptions, which we aim to continuously improve and optimize. With that purpose, we leverage large volumes of structured and unstructured data - in the form of both images and natural language - by applying state-of-the-art machine learning/deep learning and operations research techniques, against a wide spectrum of problems.
While we have many projects in delivery, we also keep a heavy focus on research, so that we always know what the best solution for a given problem is.
We are looking for someone who is a self-starter, with relevant experience successfully leading teams and developing people.
WHAT YOU'LL DO
- Hire, coach, and lead a team of talented data scientists to deliver value;
- Develop each data scientist in order to achieve A+ performance, motivation and happiness;
- Define your team’s roadmap in partnership with business and technology partners;
- Identify opportunities to improve the customer experience driven by operations;
- Identify opportunities to automate operations;
- Engage with our technology partners (e.g., data engineers, infrastructure) to refine the road to production;
- Communicate complex solutions in a clear and understandable way to both experts and non-experts.
WHO YOU ARE
- MSc or Ph.D. in a quantitative discipline: Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering;
- You are a Data scientist with 7+ years of experience in creating teams, organizing analytical workflows and mentoring junior team members;
- Practical understanding and hands-on experience with machine learning and operations research:
- Supervised learning methods (linear and logistic regression, decision trees, random forests, boosting methods, graphical models, neural networks/deep learning, etc);
- NLP and Computer Vision methods (word embeddings, BERT, ResNet, etc);
- Mathematical optimization (mixed integer programming, linear programming, stochastic programming/optimization, etc.);
- Modelling and simulation (Monte Carlo methods, Markov Chains, Markov Decision Processes, etc.);
- Experience with identifying the best metrics (both business and technical) for monitoring live machine learning services.
- Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations;
- Ability to review production level code in Python;
- Interest and passion for big data technologies (e.g., Hadoop, Spark, Hive, Cloud services);
- Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our organization;
- Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment;
- Fun to work with.