At a Glance
- Tasks: Join a dynamic team to optimise trade processes and enhance market data quality.
- Company: Leading Financial Services firm in the heart of London.
- Benefits: Gain hands-on experience, competitive salary, and opportunities for professional growth.
- Other info: Collaborate with industry experts and enjoy a vibrant work culture.
- Why this job: Make a real impact in finance while developing your data science skills.
- Qualifications: Degree in a quantitative field and strong Python and SQL skills required.
The predicted salary is between 30000 - 40000 £ per year.
Overview
On this programme, you’ll experience working in London within a Financial Services company.
After, you will move to the client site, helping to deliver key outcomes alongside industry professionals.
You will work closely with senior data scientists, product managers, and brokerage desks to design solutions that optimise trade lifecycle efficiency, enhance market data quality, and automate legacy workflows.
Build and maintain scalable data pipelines using Python and SQL to ingest, process, and store real-time and historical market data.
Analyze internal operational metrics (e. g., trade processing times, system latency, and STP/Straight-Through Processing rates) to identify bottlenecks and operational improvements.
Assist in developing machine learning models and predictive analytics for post-trade services, compliance monitoring, and data quality assurance.
Partner with global sales, broking, and technology teams to understand business requirements and translate them into robust data models.
Create intuitive data visualisations and automated reporting dashboards (e. g., using Tableau, Power BI, or Python-based UIs) to provide actionable insights for operations management.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Finance, or a related quantitative discipline.
- Strong coding skills in Python, alongside proficiency in querying and managing data with SQL.
- Familiarity with data manipulation libraries (Pandas, Num Py) and machine learning frameworks (Scikit-Learn, Tensor Flow, or Py Torch).
- Basic understanding of version control (Git) and BI/visualisation tools.
- Advanced Excel capabilities are highly valued for trading desk integration.
- Exceptional analytical and problem-solving capabilities, with a high degree of attention to detail and data integrity.
- Knowledge of financial markets (Fixed Income, OTC derivatives, or FX) and an understanding of front-to-back trading operations are significant advantages.
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