Data Scientist
Há 4 dias
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 operate a modular end-to-end technology platform purpose-built to connect the luxury fashion ecosystem worldwide, addressing complex challenges and enjoying it. We're empowered to break traditions and revolutionise, with the freedom and autonomy to make a difference for our customers all over the world.
The RoleAs a Data Analytics Engineer, you will collaborate with data analysts, data engineers, and product managers to optimise our experimentation and Data infrastructure, ensuring robustness, scalability, and flexibility to meet the analytical needs of Farfetch teams globally. Your primary focus will be on the evolution of our award-winning in-house experimentation platform, particularly advancing our custom statistical engine that processes test results and enables self-service, automated analytics for teams across the company.
Main Responsibilities:- Experimentation Platform Development: Work closely with cross-functional teams to continuously enhance our in-house experimentation platform, including building and refining features within our custom statistical engine.
- Statistical Processing: Support and enhance the platform's ability to process large-scale statistical tests, including A/B and multivariate tests, with a focus on delivering accurate, actionable insights across teams.
- Data Engineering: Design and implement efficient, scalable data pipelines and ETL processes for experimentation data, ensuring reliable data flow from ingestion through to final analysis.
- Skillful users can perform automated deep dives and access critical insights independently thanks to Self-Service Analytics.
- A strong Python programming skillset, especially in libraries commonly used in data engineering and statistical analysis (e.g., pandas, NumPy, SciPy).
- A proficient understanding of SQL and experience in data pipeline frameworks (e.g., Airflow, dbt), ETL processes, and big data technologies (e.g., Spark).
- An analytical mindset, enabling you to understand statistical concepts relevant to experimentation (e.g., confidence intervals, p-values, hypothesis testing) and forecasting models, capable of implementing these concepts programmatically.
- The capability to develop and maintain robust, scalable, and efficient data models that support business intelligence, analytics, and operational workflows, ensuring alignment with business requirements and industry best practices.
- Strong interpersonal and communication skills to work effectively across teams and support both technical and non-technical users.
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