Operations Manager Solar Orbiter MAG

Imperial College London
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Operations Engineer

AI Product Manager - Data Science (Energy) - London

AI Product Manager - Data Science (Energy) - London

Sr. Project Manager/Program Manager - Digital Twin / AIOps (OSS)

Informatics Data Science Manager

Sr Product Manager, Data Science

The Space, Plasma and Climate Community in Imperial College London’s Department of Physics is in search of an Operations Manager to lead the team operating the magnetic field instrument (MAG) on the European Space Agency Solar Orbiter spacecraft.

We build and operate state of the art space instruments for the European Space Agency and NASA.

Are you interested in space exploration and cutting edge science? Do you have an impressive background in writing high quality software for data processing, excellent attention to detail and a proven track record operating critical production software applications? Can you lead the operations of the Solar Orbiter MAG instrument?

The Mission

Solar Orbiter was launched by NASA in 2020, and has spent 4 years in the inner solar system, collecting magnetic field data and performing close fly-bys of the Sun. In February 2025, it will perform a fly-by of Venus to incline its orbit and enable the first ever images of the Sun’s poles.

This role empowers scientists to study the Sun-Earth interaction, the transfer of energy from the Sun into space, space weather and energetic particle acceleration.


Duties and responsibilities

As Operations Manager, you will:

Operate and develop the Python and MATLAB software data pipeline that turns the raw telemetry data from space into high quality public science. You will be a DevOps engineer migrating our code to a new hybrid cloud data platform Craft precise command sequences for the MAG instrument in space, maximising the data we collect and can transmit back to Earth Manage the Data Scientist who will calibrate the science data, together ensuring a reliable delivery of data to the scientific community


The ideal candidate

You have a background in data processing or have worked with experimental data from scientific instrumentation. You are passionate about data integrity and validity and have managed production software environments You have a track record of technical line management or mentoring You have experience in programming in Python or Matlab, ideally in a team environment, using recognised coding standards An interest in space measurements, especially magnetic field data, is highly desirable, however, we are also interested in hearing from data analysts from any field who take pride in doing a good job, excel at problem solving and like to be challenged

Essential requirements

Degree (or equivalent research, industrial or commercial experience) in physics, computer science, engineering, or a closely related discipline with significant exposure to software for science instrumentation or research applications Demonstrated experience of working in a team, ideally in a leadership role, to deliver a technical project within a required deadline Excellent written and verbal communication skills


The prospect of collaborating in a diverse team of intelligent and skilled engineers and scientists at the Imperial College Space Magnetometer Lab, building, testing and operating instruments for NASA and ESAThe chance to routinely command and operate a world class science instrument in spaceThe opportunity to continue your career at a world-leading institution Sector-leading salary and remuneration package (including 38 days off a year)

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 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.

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

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.