AD and NCAP Performance and Test (969)

Morson Talent
Wharley End
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Learning and Training Sector

Data Analyst - Learning and Training Sector

Credit Risk and Data Analyst

HR Systems and Data Analyst

HR Systems and Data Analyst

Data Analyst (FCS94)

AD and NCAP Performance and Test (969) Cranfield £27.94 per hour We are recruiting for an AD and NCAP Performance and Test for one of our automotive clients based in Cranfield. You will be working for a globally recognised automotive company. This is a long-term contract role. The contract is on an ongoing basis a may run for several years. Key Responsibilities of the ADAS Project Engineer: Support advanced engineering of AD and NCAP: • Support AD development • Gather data to define AD development scope • Retrofit and reprogram vehicle electronics for development test • Collect data and make initial analysis report • Facilitate alignment of specification fix between various departments within the company • Support NCAP activities • Participate in NCAP meetings for sharing of latest information • Actively make contribution to NCAP working group • Feed NCAP latest information to ADAS teams and align on latest internal strategy • Manage test activities (location, schedule, prototype vehicles, personnel and equipment) • Travel to test site in order to oversee/support testing activities. • Data collection report, data analysis report, fixed specification documentation • NCAP latest information report, NCAP development plan, NCAP test result, test result analysis. Qualifications & Experience for the ADAS Project Engineer: Be degree qualified (or equivalent) in a relevant discipline. • Have understanding of vehicle AD/ADAS systems and its development. • Have fluent written and verbal English language capability. • Have experience of software application into vehicle ECUs in the manufacturing environment – flashing, coding/configuration and calibration. • Be able to risk assess software modifications and develop appropriate test plans • Understand network communications and multiple bus principles (CAN, LIN, Ethernet). • Be able to operate CANalyzer, CAPL, CANoe, CANape, Matlab software or similar for capturing data, analysis of data and preparing/executing test procedures. • Be able to follow system circuit diagrams and identify electrical connections. • Be able to demonstrate and have practical experience in problem solving tools and techniques. • Hold a valid UK driver’s license and be able to pass driver training in order to drive vehicles for ADAS evaluation. • Have relevant experience in automotive electronic system development, completing a minimum of two vehicle developments, or be able to demonstrate equivalent experience. • Have good written and verbal communication. • Be able to travel abroad or within the UK, occasionally a few days, but on rare occasions travel may be long distance and for up to 10 weeks at short notice. • Competent with PC applications. Including Microsoft excel, powerpoint and project. • Self-motivating and self-managing. Pro-active. Energetic personality and flexible regarding working practice and working hours. • Ability to priorities and multitask between multiple projects simultaneously. LMIND

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.