We’re a pan-European digital wealth manager with 130,000 active investors (growing fast!) and over €5 billion invested on our platform. With 220+ people across 4 offices in Italy and the UK, we’re supported and funded by Poste Italiane, Cabot Square Capital, United Ventures and Allianz. We started in 2011 in Milan with a simple vision - to help more people improve their financial well-being by making personal investing straightforward and accessible through technology. Fast forward a few years, and we’re known as one of the most innovative fintechs headquartered in the heart of London.
Mission
To provide investment solutions and advice to protect and grow client wealth through time.
Our Core Values:
We’ve built our business on three Principles:
- Relationships are our first asset: We’re one team, built on trust, honesty and transparency. We value our relationships above all else.
- Trust drives success: We give each other the space to grow. We empower our employees to succeed, so they can make a real impact.
- Our customers dream big, just like us: We see the bigger picture and we make sure our customers see it, too. We’re always focused on the best outcomes for our clients and for each other, no matter what the goal, or how big the dream
What this means in practice:
At Moneyfarm, diversity is the foundation of our competitive advantage. We value our employees for who they are – their backgrounds, experiences, talents, knowledge and individual differences. This is what makes us better at what we do. To accommodate our different needs and commitments, we offer flexible working to all. Our individual impact and output is what counts most.
About the role:
We are seeking a bright and motivated Investments Data Scientist to join our dynamic Investment Team. 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.
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. You will work closely with portfolio managers and investment analysts, helping to translate complex data into actionable insights.
Key Responsibilities
- 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.
Qualifications and Skills
- 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.
Preferred
- Familiarity with financial data providers such as Bloomberg, Refinitiv Eikon, or FactSet.
- Exposure to cloud computing platforms (e.g., AWS, GCP) or big data technologies (e.g., Spark).
- An interest in deep learning frameworks (e.g., TensorFlow, PyTorch).
- Progress towards the CFA or FRM designation is a plus.
What We offer
- The opportunity to make a direct and meaningful impact on investment decisions at a leading firm.
- A collaborative, intellectually stimulating environment that encourages continuous learning and innovation.
- Strong mentorship and clear pathways for career progression and professional development.
- Access to extensive datasets and existing quantitative stack.
- Health Insurance, Wellness plan
- Fee free investments on Moneyfarm platform
- Incentive scheme
- Career development opportunities
- Training opportunities
- Regular office social events
- Happy and friendly culture!
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