At a Glance
- Tasks: Optimise trading data pipelines and create interactive dashboards for market analysis.
- Company: Dynamic financial institution focused on innovative data solutions.
- Benefits: Competitive salary, flexible working options, and opportunities for skill development.
- Other info: Collaborative environment with exposure to diverse financial products and career growth.
- Why this job: Make a real impact in financial markets while enhancing your data science skills.
- Qualifications: Advanced data science experience, SQL and Python proficiency, and understanding of financial markets.
The predicted salary is between 60000 - 80000 £ per year.
Leading the optimisation of high-performance pipelines of trading and position data, transforming millions of daily records into model-ready datasets for risk and market analysis.
Owning the design and insights from interactive dashboards and web apps, turning unique regulatory markets data into system-wide views across fixed income, commodities, FX and equities, helping stakeholders build a detailed understanding of firm and sector risks through flow and exposure data.
Leading analysis on diverse and stimulating issues, empowering market abuse, policy and supervisory teams to make informed, timely decisions whilst gaining exposure to a broad range of markets and financial products.
Supporting colleagues and growing your technical skills to impactful challenges and play a key role in shaping how data are harnessed to monitor and supervise firms and markets.
Qualifications
- Demonstrable and advanced data science and engineering experience within a financial markets context.
- Able to design efficient SQL (Redshift) and Python code for complex ETL pipelines and feature engineering.
- Substantial experience with joining, analysing, manipulating commercial and regulatory data for visualisation (Tableau) and apps.
- Demonstrable experience in utilising data analytics tools and libraries (Pandas, PySpark, etc).
- Understanding of financial markets.
- Experience in applying machine learning techniques to market data.
- Experienced in developing user-friendly analytical products, e.g. self-service tools, or web applications.
- Ability to effectively engage with a range of stakeholders.
