MASS Consultants | Senior Countermeasure Development Engineer

MASS Consultants
St Neots
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst Sr

Data Engineer

SCM Data Analyst

Senior Countermeasure Development Engineer St Neots (PE19)


  • 25 days annual leave, inclusive of up to 3 days December shut-down
  • Buy or sell up to 5 days annual leave
  • Two pension schemes to choose from
  • Private medical & dental Insurance + discounts for additional family members
  • Life Assurance scheme up to 4 x salary
  • Multiple save schemes
  • Electric/hybrid car leasing scheme
  • Cycle to work scheme
  • Retail discounts
  • Continuous professional & personal development support
  • Annual Wellness Allowance


Forming part of our Electronic Warfare Support Group (EWOS), our Countermeasure Development team provide technical advice and support to our customers relating to countermeasure development, engineering support and delivery, as well as supporting internal initiatives relating to innovation and research, projects, training and bid delivery.


You will have an opportunity to develop our in-house countermeasure simulation software tool, CounterWorX, modelling physical interactions and phenomena across dynamic simulations. This opportunity will also expose you to working with our software development team, and the assurance process all releasable software products must follow.


The team are based across two MASS sites (head office, Enterprise House, in Little Paxton, and our modern Lincoln offices on Teal Park).When project classifications allow, they tend to work three days in the office and two days from home, giving you the best of both worlds: flexibility and collaboration.


How youll support us


As a senior modeller within the team, you will have the autonomy to develop models, add new features into the countermeasure software, and solving customer challenges and requirements. You will be responsible for managing technical work for junior team members, as well as line management of some of the junior engineers.


This is a rapidly growing team, allowing for career development opportunities within the team and across the broader EWOS group.


The invaluable experience youll bring, to help us achieve more


Core to this role will be your detailed understanding of Physics/Mathematical principles at degree-level, as well as experience with at least one of Electro-Optics (EO), Infra-Red (IR), or Radio Frequency (RF) sensors and systems, within a defence setting.


The role will allow you to analyse, design, model (MATLAB/Python/Simulink), and verify countermeasure techniques across a variety of real-world scenarios, culminating in delivery to customers for use in operation.


Your experience developing either deterministic, stochastic, or exploratory models will help optimise countermeasure parameters for best performance against specific threats. Additionally, your ability to capture modelling requirements and ensure delivery against these will be crucial.


Essential experience


  • Experience using programming software (MATLAB/Simulink/Python etc.).
  • Allocation of work packages/tasks to junior team members.
  • Experience in at least two stages of model development (Design, Build, Integration, Test, Deployment etc.).
  • Understanding of modelling techniques (stochastic, deterministic, exploratory etc.).
  • Generation of simulation requirements, configuration and analysis reports.


Desirable experience


  • Experience modelling/programming RF/EOIR countermeasure techniques (platform protection).
  • Experience line managing a team.
  • Knowledge of either mathematics or physics involved in Radar, Electro-Optics or Infra-Red system operation.
  • Interest in new technologies and their application within an EW domain.
  • Mentoring and coaching junior team members.
  • Experience optimising model performance.


Our non-negotiables:

Due to the highly secure nature of the projects that you will be involved with, youmustbe:

  • A UK National and eligible to work in the UK
  • Eligible to obtain and maintain a UK GovernmentSecurity Check (SC) clearance.


Who is MASS


MASS is an independent, global technology company, trusted by highly secure organisations to provide advanced, digital services that manage data and keep information safe. With our heritage in defence, we offer robust solutions to sectors where security expertise is essential.


Wellbeing is at the core to our culture, allowingemployees to flourish and to achieve their full potential. Our people are important to us, and we take pride in our wellbeing programmes and policies that supportindividuals including, mental health first aiders and readily available support through our extensive employee assistance programme.


We work in partnership with customers, using skilled, technical experts. We think innovatively to provide tailored, agile and resilient solutions that secure advantage, so youre ready for digital transformation. MASS is an equal opportunities employer; we know that our people are smart, skilled and motivated and in return we provide a friendly workplace where everyone is valued and has the chance to make an impact.

Apply todayto see how working for MASS could work for you!


JBRP1_UKTJ

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.