Data Scientist, Moneyfarm

• We are seeking a bright and motivated Investments Data Scientist to join our dynamic Investment Team
• This position is ideal for a candidate with a strong foundational understanding of modern quantitative methods, coupled with a passion for financial markets and some previous exposure to the sector
• Degree (MSc or PhD) in a quantitative discipline such as Financial Engineering, Statistics, Computer Science, Physics, Mathematics, or a related field
• Up to 5 years of relevant experience (including internships or academic projects) in a quantitative or data-focused role
• Strong proficiency in Python and its data science ecosystem (pandas, NumPy, SciPy, scikit-learn, statsmodels)
• Solid understanding of financial time series modelling, including concepts related to forecasting, volatility, and non-stationarity
• Demonstrable experience applying machine learning techniques (e.g., Gradient Boosting, Random Forests, Clustering) to data, preferably financial
• Experience with (or academic exposure to) building investment signals or automating data analysis and reporting
• Proficiency in SQL for querying and managing large datasets

Desirable

• This is an excellent opportunity to grow your career and apply your quantitative skills to real-world investment challenges
• You will support senior team members in designing, developing, and deploying sophisticated models that influence investment decisions
• You will work closely with portfolio managers and investment analysts, helping to translate complex data into actionable insights
• Automate Reporting: Support the building and automation of investment reports and financial reports, helping to provide timely and accurate insights to portfolio managers and stakeholders
• Support Model Development: Assist in the design, backtesting, and implementation of statistical and machine learning models for asset allocation, risk management, and return forecasting
• Conduct Data Analysis: Perform rigorous analysis of financial time series to help model market dynamics, understand volatility patterns, and identify underlying trends
• Assist in Signal Generation: Contribute to the research, design, and validation of predictive investment signals by working with a wide range of traditional and alternative financial data
• Contribute to Research: Assist in researching cutting-edge academic and industry findings in quantitative finance and machine learning
• Support Portfolio Managers: Generate insights for Portfolio Managers through analysis of portfolio performance, risk, and performance attribution
• Collaboration & Communication: Work collaboratively with the team to integrate quantitative insights into the investment process