Full-time
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Job description
Company Insight:
A leading global trading firm is expanding its alternative data initiative and is seeking an experienced Data Scientist to help drive the next phase of signal discovery and deployment. Sitting at the intersection of quantitative research and live trading, you'll work with datasets that are large, messy and unstructured - using rigorous analytical methods to determine what is predictive, what scales, and what ultimately makes it into production.
The team operates in a research-driven environment with fast feedback loops, close proximity to trading, and a strong emphasis on measurable impact rather than theoretical modelling.
The Role:
- Evaluate and analyse a wide range of alternative datasets (transactional, behavioural, geospatial, web-derived, etc.) to identify potential trading signals
- Design and run experiments to assess data quality, stability, coverage and predictive power
- Build statistical and machine learning models to transform raw alternative data into usable features and signals
- Develop robust analytical frameworks to monitor signal decay, bias and performance over time
- Communicate findings clearly to quantitative researchers and key stakeholders, influencing research and trading priorities
Experience/Skills Required:
- A strong academic background in a quantitative field from a top-tier university
- 2–5 years of experience in a data science or research role within a hedge fund, market maker, or investment bank
- Demonstrated experience working with alternative data and assessing its real-world usefulness
- Advanced Python skills and strong SQL capability for large-scale data analysis
- A solid understanding of statistics, experimentation, and applied machine learning in noisy environments
Pre-Application:
- Please do not apply if you are looking for a contract or remote work
- Please ensure you meet the required experience section prior to applying
- Allow 1-5 working days for a response to any job enquiry
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Scientist?
A: To succeed as a Data Scientist, one must possess core technical skills such as proficiency in programming languages like Python, R, or SQL, as well as expertise in machine learning algorithms, data visualization tools like Tableau or Power BI, and statistical modeling techniques. Additionally, strong soft skills like effective communication, collaboration, and problem-solving abilities, along with traits like curiosity, adaptability, and attention to detail, are crucial for success in this role. By combining these technical and soft skills, Data Scientists can effectively extract insights from complex data, drive business decisions, and drive career growth through continuous learning and innovation.
Q: What is the career path for a Data Scientist?
A: A Data Scientist's typical career progression involves starting as a Junior Data Analyst or Data Scientist, where they develop foundational skills in data analysis, machine learning, and visualization. As they gain experience, they can move into mid-level roles such as Senior Data Scientist or Lead Data Analyst, where they take on more complex projects, mentor junior team members, and contribute to strategic decision-making. Ultimately, senior Data Scientists can transition into leadership positions like Director of Data Science or Chief Data Officer, or pursue specialized roles like Data Engineering or Artificial Intelligence Research Scientist, depending on their interests and skills.