Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Quantitative Implementation Analyst

Seven Investment Management LLP
City of London
2 weeks ago
Create job alert

This is a junior to mid-level role supporting the Portfolio Management team at Seven Investment Management. There is a focus on helping to manage data and build processes to improve the quality and scalability of the team’s investment process, including of implementation across the firm’s funds and models. The new hire will also be contributing to the development of tools to assist in manager selection, drawing from their own market knowledge.


Although some level of programming experience is beneficial, more so is an enthusiasm to apply and learn new skills relating to quantitative approaches to portfolio management. The role offers a great stepping stone into a more quantitatively oriented role within the investment management space.


Responsibilities

  • Support team members in developing tools to help guide instrument selection and portfolio construction within 7IM’s multi-asset investment process.
  • Support trading activities within the firm’s multi asset funds.
  • Manage the team’s data creation and storage, ensuring all target position data are uploaded in a timely fashion and integrity is maintained to the highest standards.
  • Support the creation of model portfolios by supplying data to the PMs on a variety of asset risk and static data.
  • Support the portfolio management team in the execution of fund trades.
  • Support the Strategy and Portfolio Management teams with ad-hoc requests in support of research projects.
  • Subscribe to 7IM’s VPVPs other Treating Customers Fairly (TCF) and SMCR requirements.

About You
Knowledge

  • An interest in investment management, including manager selection within the active manager space.
  • Keen to develop an understanding of investment portfolio theory, portfolio construction and risk techniques in a multi-asset context
  • An understanding of factor risk models will be an advantage, including the ability or willingness to learn how to build models from scratch.
  • Experience of working with a trading system, such as Bloomberg AIM would be advantageous.

Qualifications

  • Masters, or strong undergraduate degree in a subject with quantitative content is preferred
  • Preferably gained, or working towards gaining, the CFA or other recognised industry qualifications.

Skills

  • Capability to learn to use judgement and formulate investment actions.
  • Ability to work as part of a team and adapt to the changing needs as appropriate
  • Be able to understand, interpret and replicate financial academic literature
  • An enquiring and curious mind willing to learn new skills and adapt to new tasks
  • Comfortable in working with large amounts of data, including querying and uploading data (preferably using a tool such as SQL)
  • Some experience in a programming language such as Python, either through studies or work, is preferred.
  • Experience working within a role supporting trading activity preferable.
  • This role is captured under the certification regime


#J-18808-Ljbffr

Related Jobs

View all jobs

Structured Credit Quantitative Analyst

Quantitative Business Analyst – Risk Technology (PFE / Credit Risk) (m/f/d)

Quantitative Risk Analyst - Commodities

Quantitative Risk Analyst - Commodities

Junior Quantitative Analyst (Structured Credit / Securitisation)

Junior Quantitative Analyst (Structured Credit / Securitisation)

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.