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For Companies At a Glance
- Tasks: Join a team ensuring accurate and timely portfolio risk analytics for investment decisions.
- Company: Fidelity Asset Management, a leader in innovative financial solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
- Why this job: Make a real impact on investment strategies using your data expertise.
- Qualifications: Bachelor's degree in a quantitative field and 3+ years in data operations.
The predicted salary is between 36000 - 60000 £ per year.
The Role
Quantitative Research and Investments (QRI) is seeking a highly motivated data expert in the domain of portfolio risk analytics to join a risk platform operations team responsible for ensuring that all vendor and internal portfolio risk analytics used for risk management and portfolio construction across Fidelity are delivered consistently, accurately and on a timely basis. The Risk Platform Operations team are the stewards of risk analytics data for Fidelity Asset Management. They focus on quality control of all data that feeds into portfolio risk analytics, including security factor exposures and proxies, factor returns and covariance matrices, fundamentals data, security T&Cs, and portfolio holdings.
In this role, you will utilize domain expertise necessary to root-cause daily issues effectively, work with internal and external data providers to resolve issues at source, answer portfolio and risk manager questions, and develop automated systems for identifying data quality issues.
The Skills and Expertise You Bring:
- Act as a steward of data assets critical to risk management and portfolio construction.
- Lead quality service efforts to address overnight data feed issues, enabling fast, seamless responses to upstream problems and insulating production and research teams.
- Update and verify multi-factor risk model inputs and outputs prior to client delivery.
- Ensure access to accurate, timely, and relevant portfolio risk analytics by collaborating with technology and business partners to resolve data quality issues at the source.
- Analyze systems and processes to identify efficiencies and improve reporting accuracy and timeliness.
- Apply experience with market risk models from vendors such as Barra, Axioma, Northfield, or Bloomberg.
- Leverage strong analytical skills to comprehend large datasets and implement effective quality controls.
- Operate with a proactive, self-motivated approach, meeting objectives with minimal direction.
- Utilize vendor-provided risk data and tools including Bloomberg PORT, BarraOne, RiskManager, and/or Axioma.
- Demonstrate deep knowledge of financial data across security, company, portfolio, and index levels, including pricing for equities, bonds, and derivatives.
- Employ technical proficiency in SQL, Python, Snowflake, and/or Oracle, along with data quality frameworks.
- Hold a Bachelor's degree (or higher) in mathematics, statistics, engineering, computer science, finance, or a related quantitative field.
- Bring 3+ years of experience in global data operations or support teams within peer firms, with a proven track record of delivering value.
- Apply expertise in anomaly detection methods, data quality workflows, and statistical best practices.
- Communicate effectively across technical and investment teams.
- Navigate complex data environments and support the necessary technology and analytics infrastructure.
- Identify root causes of data quality issues and collaborate with teams and providers to resolve them.
- Create automated processes to detect errors and ensure high-quality data for investment decision-making.
- Document procedures and validate data to maintain transparency and reliability.
- Possess domain expertise in investment management across risk management, portfolio management, trading, and investment operations.
The Team:
The Risk Platform Operations team is an integral part of the Quantitative Research and Investing (QRI) division in Asset Management. QRI is responsible for the management and development of quantitative investment strategies and solutions while providing high quality quantitative, data-driven support to Fidelity's fundamental investment professionals, ensuring they have access to the most relevant data and advanced quantitative analysis.
Certifications:
Category: Data Analytics and Insights
Associate, Quantitative Data Operations in London employer: Fidelity
Contact Detail:
Fidelity Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Associate, Quantitative Data Operations in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working in risk analytics or data operations. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a presentation that highlights your experience with data quality frameworks and market risk models. This will help you stand out during interviews and showcase your technical acumen.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions related to data operations and risk management. Mock interviews with friends or mentors can help you refine your answers and boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate individuals who can contribute to our team. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Associate, Quantitative Data Operations in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the skills and expertise mentioned in the job description. Highlight your experience with data operations, risk analytics, and any relevant technical skills like SQL or Python. We want to see how you fit into our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about portfolio risk analytics and how your background makes you a perfect fit for the role. Don’t forget to mention your proactive approach and problem-solving skills!
Showcase Your Analytical Skills: In your application, provide examples of how you've used your analytical skills to solve complex data issues or improve processes. We love seeing real-world applications of your expertise, especially if they relate to risk management or data quality.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy to do!
How to prepare for a job interview at Fidelity
✨Know Your Data Inside Out
Make sure you brush up on your knowledge of portfolio risk analytics and the specific data types mentioned in the job description. Familiarise yourself with terms like security factor exposures, covariance matrices, and multi-factor risk models. This will show that you’re not just a data expert but also someone who understands the nuances of the role.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with SQL, Python, and any other relevant tools like Bloomberg PORT or Axioma. Bring examples of how you've used these skills to solve data quality issues or improve reporting accuracy. This will demonstrate your technical acumen and problem-solving abilities.
✨Prepare for Scenario Questions
Expect questions that ask you to describe how you would handle specific data quality issues or anomalies. Think through past experiences where you identified root causes and resolved problems. Use the STAR method (Situation, Task, Action, Result) to structure your answers clearly and effectively.
✨Communicate Clearly and Confidently
Since the role involves collaboration with various teams, practice explaining complex data concepts in simple terms. Be ready to discuss how you’ve communicated effectively across technical and investment teams in the past. This will highlight your ability to bridge gaps between different stakeholders.