Data Scientist

Marcus Webb Associates Limited
London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist - Renewable Energy

Our clients’ technologies will revolutionise data centres. Their technologies will speed up training and inference while dramatically reducing energy consumption and supporting a sustainable future.

They are looking for Data Scientist to develop metaheuristic optimisation algorithms and intelligent test frameworks for optical network systems. This cross-disciplinary role blends software engineering, algorithm development and hardware test integration to reduce test time, improve throughput and enhance performance analysis.

This role exists within their product systems team. They are involved with integrating different hardware platforms and components from different teams, into one final product. At this point the systems must be tested, validated and optimised in readiness for production / NPI.

There are two main elements of this role: System optimisation and automation / enhancement of test processes.

Responsibilities: Data Scientist

  • Develop metaheuristic, data-driven optimisation algorithms (e.g., genetic algorithms, simulated annealing, swarm optimisation) to reduce test time and improve measurement efficiency.

  • Design and implement automated test frameworks for high-speed optical network system, integrating hardware instrumentation.

  • Analyse large datasets from validation and production testing to identify performance trends, bottlenecks, and opportunities for improvement.

  • Work with hardware engineers to optimise burst-mode test sequences, equalisation settings (CTLE, FFE, DFE), and link tuning strategies.

  • Implement adaptive, hardware-aware test routines that adjust dynamically based on device behaviour.

  • Support the integration of optimised test flows into high-volume manufacturing environments.

  • Maintain scalable, modular software architectures for future test platforms.

    Skills & Experience: Data Scientist

  • Experience with metaheuristic algorithm optimisation (e.g., Genetic Algorithms, simulated annealing, particle swarm).

  • Collaborative mindset to work closely with hardware engineers and manufacturing teams.

  • Familiarity with production test time optimisation in similar environments e.g. semiconductor, optical transceivers, photonics, network and data centre hardware, telecoms systems, storage / servers / HPC, consumer electronics or specialist test and measurement products or computer vision.

  • Proficiency in software development for test automation (Python, C++, or C#).

  • Exposure to cloud-based data pipelines for large-scale test data processing.

  • Experience with AI/ML techniques (e.g., reinforcement learning, predictive modelling) for test optimisation.

  • Degree or PhD in Computer Science, Electrical/Electronic Engineering, Applied Mathematics, Data Science or related field.

    This role is based in Central London close to Whitechapel and offers hybrid working however candidates must be within a commutable distance.

    This position would suit an Optimisation Analyst, Machine Learning Scientist, Data Scientist or Optimisation Engineer with experience in optimising data rather than just modelling

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.