Senior Machine Learning Engineer - Earth Observation - Remote UK

Energy Aspects
Leeds
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - (Python & SQL)

Senior Data Scientist

Lead Data Engineer

Senior Data Scientist

Junior / Graduate Data Scientist

Actuarial Data Scientist

Energy Aspects currently have an exciting opportunity available for a Senior Machine Learning Engineer to join our Earth Observation team.

The role offers a rare opportunity to work on developing novel products for the oil & gas industry. You will develop and manage projects that make use of Earth Observation data and are applied to solve problems in the oil & gas industry. You will turn ideas into project plans, technical specifications and personally develop rapid proof-of-concept implementations using your strong technical skillset.


Key Responsibilities:


  • Work with internal or external oil & gas experts to develop end-to-end EO applications


  • Manage and develop projects from idea into proof-of-concept working solutions quickly and pragmatically


  • Effectively communicate with senior leaders on technical topics, capturing requirements with ease and translating into practical solutions



Requirements:


  • 5+ years' experience in applying image processing/computer vision to practical business applications


  • Experience managing product development


  • Practical experience with ML models for image processing tasks (object detection, image segmentation)


  • Advanced Python skillset, familiar with object-oriented development and software development best practices


  • Expert knowledge of the Python modules: GDAL, OpenCV, Numpy, Scikit-Learn, Matplotlib, Pandas, GeoPandas


  • Practical experience with geographical data analysis and GIS software


  • Degree in an engineering or quantitative subject


  • Excellent communication skills, experience working alongside and presenting to senior leadership



Desirable Skills:


  • Experience with version control, DevOps, and testing


  • Experience in using relational databases, especially PostgreSQL using SQLAlchemy


  • Experience with cloud platforms such as AWS, Google Cloud Platform


  • Experience in Deep Learning, or other AI domains



Please note that this is a UK-based remote role with a fixed-term contract of 2 years.


Job Benefits


Welcome to our unique workplace where a passion for our industry-leading product sits at the heart of who we are. Life at EA is completely eclectic, fostered through the global nature of the business and a real appreciation of the many cultures of our diverse team.


We recognise your contribution with a competitive compensation package that includes annual bonuses, comprehensive private health insurance, and substantial pension contributions. Additionally, we offer subsidised gym memberships, and a generous holiday policy to support your financial and personal well-being.


Join a company that values your professional growth and personal fulfilment, all within a supportive and engaging environment.

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.