Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join a dynamic team ensuring top-notch data quality for portfolio risk analytics.
- Company: Fidelity, a leader in asset management and quantitative research.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact on investment strategies with cutting-edge data analytics.
- Qualifications: Bachelor's degree in a quantitative field and 3+ years in data operations.
- Other info: Collaborative environment with a focus on innovation and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
Overview
Quantitative Research and Investments (QRI) is seeking a highly motivated data expert in 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 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.
Responsibilities
- 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.
- 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.
Qualifications
- 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.
Associate, Quantitative Data Operations in Westminster employer: Fidelity Investments
Contact Detail:
Fidelity Investments Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Associate, Quantitative Data Operations in Westminster
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for interviews by practising common questions and scenarios related to data operations. We recommend doing mock interviews with friends or using online platforms to get comfortable with your responses.
✨Tip Number 3
Showcase your skills! Create a portfolio or GitHub repository that highlights your projects, especially those involving SQL, Python, or data quality frameworks. This gives potential employers a tangible look at what you can do.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Associate, Quantitative Data Operations in Westminster
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Associate in Quantitative Data Operations. Highlight your experience with data quality, risk analytics, and any relevant technical skills like SQL or Python. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data operations and how your skills can contribute to our Risk Platform Operations team. Keep it concise but impactful – we love a good story!
Showcase Your Analytical Skills: In your application, don’t forget to mention specific examples where you've used your analytical skills to solve problems or improve processes. We’re all about data-driven decisions, so let us know how you’ve made an impact in previous roles!
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 get the best experience possible. Plus, it shows you're keen on joining our team at StudySmarter!
How to prepare for a job interview at Fidelity Investments
✨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 understand the context in which that data is used.
✨Demonstrate Problem-Solving Skills
Prepare to discuss specific examples where you've identified and resolved data quality issues. Think about how you approached root-cause analysis and what steps you took to ensure accuracy. This will highlight your proactive approach and ability to work under minimal direction, which is crucial for this role.
✨Showcase Technical Proficiency
Be ready to talk about your experience with SQL, Python, and any other relevant tools like Bloomberg PORT or Axioma. You might even want to prepare a few technical questions to ask the interviewers, demonstrating your enthusiasm and expertise in using these technologies to improve data quality.
✨Communicate Clearly and Effectively
Since the role involves collaboration with both technical and investment teams, practice explaining complex data concepts in simple terms. This will help you convey your ideas clearly during the interview and show that you can bridge the gap between different teams effectively.
