Product Data Science Lead
Octopus Energy
Data Scientist, Quantitative Strategies – Asset & Wealth Management
Moneyfarm
full-time
Posted on:
Location Type: Hybrid
Location: London • 🇬🇧 United Kingdom
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
AWSCloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
- 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.
Requirements
- 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.
- 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.
Benefits
- 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!
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonpandasNumPySciPyscikit-learnstatsmodelsSQLmachine learningfinancial time series modellingdata analysis
Soft skills
collaborationcommunication
Certifications
CFAFRM
