Investment Data Analyst

About the Role

Our client, a prominent Asset Management firm situated in London, is searching for a driven data specialist to join their Risk Data Operations team. This team ensures that all vendor and internal portfolio risk analytics integral to risk oversight and portfolio formation are delivered reliably, accurately, and on schedule. While previous expertise in risk analytics data is not mandatory, familiarity with asset management environments and proficiency in Python alongside developing advanced data workflow automation is essential.

Key Responsibilities

• Safeguard critical data assets that underpin risk management and portfolio construction.
• Lead rapid response efforts for overnight data feed disruptions, ensuring smooth operations for production and research teams.
• Carry out updates and verifications of multi-factor risk model inputs and outputs before delivery to clients.
• Collaborate with technology and business units to resolve data quality issues at their origin, ensuring portfolio risk analytics are accurate and timely.
• Evaluate existing systems and procedures to boost efficiency and improve the timeliness and precision of reporting.
• Utilize knowledge of market risk models from vendors including Barra, Axioma, Northfield, and Bloomberg.
• Use strong analytical abilities to understand extensive datasets and execute robust quality controls.
• Operate independently with a proactive mindset to meet objectives with minimal guidance.
• Employ vendor risk data and software tools such as Bloomberg PORT, BarraOne, RiskManager, and Axioma.
• Apply extensive knowledge of financial data covering securities, companies, portfolios, and indices, including equities, bonds, and derivatives pricing.
• Exhibit technical skills in SQL, Python, Snowflake, Oracle, and data quality frameworks.
• Maintain thorough documentation and validation procedures to uphold transparency and dependability.
• Possess domain knowledge across investment management facets including risk, portfolio, trading, and investment operations.

Qualifications & Experience

• Bachelor's degree or higher in quantitative disciplines such as mathematics, statistics, engineering, computer science, finance, or similar fields.
• Background in global data operations or support roles within similar firms, demonstrating a history of delivering meaningful outcomes.
• Experience with anomaly detection approaches, data quality workflow management, and statistical best practices.
• Ability to effectively communicate with both technical and investment-focused teams.
• Skilled at diagnosing root causes of data quality problems and collaborating with internal teams and external providers for resolutions.
• Capable of developing automation to detect anomalies and maintain high-quality datasets supporting investment decisions.