About the role

Data Scientist developing predictive financial models embedded in Commercial Finance at Farfetch. Utilizing advanced modeling to drive strategic insights and financial performance in luxury fashion.

Responsibilities

Apply sophisticated statistical methods, time series analysis, and machine learning techniques to model financial performance, optimize pricing, analyze budget variances, and forecast key financial metrics.
Extract, clean, and transform complex, high-volume financial and operational datasets to identify trends and actionable opportunities.
Design and rigorously execute A/B tests and causal inference studies to measure the financial impact of changes in pricing, marketing investments, and operational efficiencies.
Translate experimental results into clear, monetizable recommendations for the Commercial Finance and Operations teams.
Serve as the primary liaison between Commercial Finance and Data Engineering to define, develop, and optimize robust data structures and pipelines specific to financial reporting and analysis needs.
Ensure data quality, integrity, and scalability for all analytical and reporting needs.
Proactively partner with Senior Finance Managers, Budget Owners, and Commercial Leaders to understand complex business requirements, define analytical problems, and present data-driven solutions that influence strategic financial decisions.
Develop and maintain dynamic dashboards, reports, and visualizations focused on tracking critical financial and commercial KPIs.
Collaborate with Engineering teams to operationalize and deploy financial and predictive models into production systems, ensuring accuracy, performance, and monitoring.

2-3 years of professional experience in a Data Scientist, Quantitative Analyst, or similar role, preferably within a Finance, Fintech, or Commercial environment.
Bachelor’s or Master’s degree in a highly quantitative field such as Statistics, Mathematics, Engineering, Financial Economics, or a related discipline.
Quantitative Foundation: Strong theoretical and practical foundation in statistical inference, econometrics, hypothesis testing, regression analysis, and machine learning principles.
Domain Expertise: Demonstrated understanding of core financial concepts, reporting (P&L, balance sheet), forecasting processes, and commercial metrics.
Technical Proficiency: Expert proficiency in programming languages and tools essential for data science: Python (Pandas, NumPy, Scikit-learn) and SQL.
Forecasting Skills: Proven experience with advanced forecasting techniques, including time-series modeling (ARIMA, Prophet, etc.) and predictive machine learning models.
Exceptional ability to translate complex analytical findings into clear, concise, and persuasive business narratives for executive-level finance and commercial audiences.
Experience with GCP and Databricks is a plus.
Familiarity with data visualization tools such as Looker, Looker Studio.
Experience working with large-scale, complex transactional and financial data will be highly valued.
A collaborative and proactive team player with a high degree of organizational skill and attention to detail.

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