Quantitative Analyst

Lorien
Perth
1 month ago
Applications closed

Related Jobs

View all jobs

Quantitative Analyst (Equities & Equity Derivatives - VP)

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Job Title: Quantitative AnalystLocation: Perth (Hybrid)Duration: 6 MonthsDescription:

This role takes responsibility to support, drive and represent EM Trading in the Systematic Trading Project that is underway with a core focus on the Strategy workflow and related dependencies. This strategic role will work closely with the Project leadership, in particular the Systematic Trading Project SME.You Will* Work closely with the Systematic Trading Project SME from Trading to provide value across the project from the lens of a Quantitative Analyst responsible for parts of the Strategy workflow in collaboration with Trading Teams* Gain insight to our platform design and provide material input and impact to the technical implementation both for the core platform and the strategy workflow* Provide dedicated support to the project as a whole including non-technical members in workstreams tackling dependencies, including prototyping examples to make theories or complicated logic become tangible examples for project stakeholders* Provide a Quantitative Analyst skillset as a link between project stakeholders and the platform developers, with particular focus time with the Trading SME* Gain direct knowledge and exposure to Commodities Trading (particularly Energy) with a wide scope from asset to proprietary, signal generation to automated execution and all related depe...

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.