Senior Electronics Engineer

Bournemouth
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist Research Engineer

Senior Data Scientist Research Engineer

Senior Data Scientist Research Engineer

Senior RF AI/ML Data Scientist — DSP & SDR Onsite

Data Engineer (Data Manager) | NHS England

Data Analyst

Senior Electronics Engineer – Bournemouth - £50k - £60k – Semi remote

Hexwired Recruitment has partnered with a well established Electronics Manufacturer in Bournemouth who are now seeking a Senior Electronics Engineer with a solid background working on Mixed signal Design.

The company is recognised globally, working with some of the biggest names in the industry. As a Senior Electronics Engineer you will be hands on with a range of bespoke systems, so the work will be varied and challenging.

As a Senior Electronics Engineer, You will be working in a multidisciplinary team, due to the nature of the work, the ability to gain security clearance is essential and the role will require some onsite work.

Key Requirements:

  • Bachelors, Masters or PhD in Electronics, Maths, Physics or similar

  • At least 3 years commercial experience in Electronics Design

  • Good experience working on Analogue and Digital Electronics design

  • Good commercial PCB design experience

  • Current or ability to gain SC clearance

    The company are looking to offer circa £60k along with an excellent benefits package. If you’re interested in this Senior Electronics Engineer job, please apply.

    For more information on this role or any other jobs across; FPGA, Mixed-signal, Electronics, Hardware, Embedded, C++ programming, Mechanical design, Analogue Eelctronics, Embedded Linux, Golang Development, Machine Learning, Data Science or Simulation contact us today

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.