Data Scientist, Commercial Finance

Role Overview

• 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.
• Ensure insights are communicated clearly and concisely to both technical and non-technical finance stakeholders.
• Collaborate with Engineering teams to operationalize and deploy financial and predictive models into production systems, ensuring accuracy, performance, and monitoring.

Requirements

• 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.

Tech Stack

• Google Cloud Platform
• Numpy
• Pandas
• Python
• Scikit-Learn
• SQL

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