Senior Quantitative Analyst

Fidelity International
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Risk Analyst

Enterprise Market Risk Quantitative Analyst (IRRBB & CSRBB), AVP

Manager Quantitative Analysis - Centre for UK Growth

Manager Quantitative Analysis - Centre for UK Growth

Manager Quantitative Analysis - Centre for UK Growth

Manager Quantitative Analysis - Centre for UK Growth

About the Opportunity

Job Type: Permanent
Application Deadline: 23 January 2026


Title Senior Quantitative Analyst


Department Systematic Investing


Location Cannon Street, London


Reports To Global Head of Quant and Portfolio Engineering


Level 8


We’re proud to have been helping our clients build better financial futures for over 50 years. How have we achieved this? By working together - and supporting each other - all over the world. So, join our team and feel like you’re part of something bigger.


About your team

Fidelity Systematic Investing (FSI) brings together our systematic capabilities across Quant, Equity, Fixed Income and Multi-Asset to deliver solutions to clients. Building a common infrastructure and operating platform to respond to the changing industry landscape and evolving client needs while leveraging Fidelity’s research platform.


About your role

The role will focus on researching improvements to the existing range of equity quant capabilities as well as developing new and innovative ideas to enhance our systematic products. They will work with the global team to help integrate research into the systematic and discretionary investment processes. Engagement with sales, marketing and distribution as well as external clients to showcase & explain our capabilities will be a key part of the role. They will also be involved in transforming research into thought leadership & white papers.



  • Develop new capabilities and products in the quant equity space - leveraging FIL Proprietary data and the latest portfolio construction techniques.
  • Work with our sales, marketing and distribution teams to develop the systematic & quant team brand. Driving the growth of the AUM business with both internal and external stakeholders.
  • Work closely with the Portfolio Engineering team on implementation of equity quant capabilities.
  • Develop enhancements to existing suite of models.
  • Conduct standalone research projects resulting in white papers & presentations for both internal and external consumption.
  • Mentor and develop more junior members of the team.
  • Collaborate with the team to enhance the existing research platform.
  • Work closely and build relationships with peers in UK, Europe and Asia across the entire firm, including the equities, multi-asset, sales & distribution, and technology teams.

About you

  • Experience of working in a quantitative equity role with a demonstrable passion and enthusiasm for investing, supported by an track record of high quality research on equity factors and portfolio construction.
  • A comprehensive understanding of equity research. Including equity factors, risk models, optimisation, sustainability integration and portfolio construction techniques.
  • Preference to have a track record of published research related to quant equity portfolios.
  • Experience working with clients, presenting research at conferences or other events and collaborating with clients on customised solution designs.
  • Demonstrable record of high ethical standards, integrity and desire to uphold Fidelity’s stated core values and behaviours.
  • An entrepreneurial self-starter, with commercial acumen, energy & business vision.
  • MSc and PhD (with knowledge of statistics, econometrics & numerical methods)
  • Excellent written and oral communication skills
  • Programming experience (preferably Python - knowledge of pandas, numpy etc…)
  • Experience in manipulating and understanding of large datasets.
  • Understanding of the latest AI techniques and experience applying them to equity portfolios.

Conduct Rule Responsibilities

The role-holder is expected to meet the following FCA Conduct Rules when performing their role under the Certification Regime.


FCA/PRA Conduct Rule (COCON) Responsibilities


I abide by the FCA’s Conduct Rules when discharging my responsibilities described above.



  • I must act with integrity
  • I must act with due care, skill and diligence
  • I must be open and cooperative with the FCA, the PRA and other regulators
  • I must pay due regard to the interests of customers and treat them fairly
  • I must observe proper standard of market conduct
  • I must act to deliver good outcomes for retail customers


  • DBS Check required for all SMCR roles
  • A recorded line is required for roles in-scope of SMCR or K&C

Feel rewarded

For starters, we’ll offer you a comprehensive benefits package. We’ll value your wellbeing and support your development. And we’ll be as flexible as we can about where and when you work – finding a balance that works for all of us. It’s all part of our commitment to making you feel motivated by the work you do and happy to be part of our team. For more about our work, our approach to dynamic working and how you could build your future here, visit careers.fidelityinternational.com.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.