Design Systems Analyst (Smart Places & Digital Twin Specialist)

Cremorne
8 months ago
Applications closed

Related Jobs

View all jobs

SharePoint M365 Data Analyst

Data Analyst

Data Analyst

MI & Data Analyst

Quantitative Analyst (Equities & Equity Derivatives - VP)

Lead Business Intelligence Analyst

Foster + Partners

Design Systems Analyst (Applied R+D Smart Places & Digital Twin Specialist)

London, Battersea

Permanent

On site

Foster + Partners is a global studio for architecture, engineering, urban and landscape design, rooted in sustainability.

The Applied Research and Development team at Foster + Partners is looking for a Design Systems Analyst (Applied R+D Smart Places & Digital Twin Specialist) to join their team in London.

This role will be responsible for:

  • Research and develop smart building, smart city and digital twin technologies. Engage with all stages of the innovation lifecycle, capturing requirements, identifying successful technologies and promoting these from prototype to production use

  • Liaise with designers and domain specialists internal and external to the company to ensure effective development, integration, and application of wider company design systems and processes

    Key skills:

  • Degree in Architecture, Engineering (including Building Services), or Computer Science or equivalent experience

  • Experience in one or more areas of collecting, managing and visualizing data related to the built environment, including: sensor technology, asset information management, cloud data management, data processing and visualisation

  • Experience in one or more of the following areas: architecture, information management, building services and smart buildings, smart cities, digital twins, ubiquitous computing (including the Internet of Things), data science, interactive application (including games engine) development, cloud computing

  • Familiarity with systems and processes for managing buildings, cities or other complex assets (BMS / AMS)

    In return we offer a competitive basic salary and generous benefits package which includes 25 days holiday (exc bank holidays), Pension, DIS and discretionary annual bonus

    If you would like to work for a company that can offer you a career then please apply by sending an up to date CV

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.