Senior AI Data Scientist

Halliburton
Abingdon
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Optimisation Data Scientist Contract

Data Scientist / AI Engineer

Lead Data Scientist

Data Scientist

Managing Consultant, Customer Data Analytics / Data Science

Managing Consultant, Customer Data Analytics / Data Science

We are looking for the right people — people who want to innovate, achieve, grow and lead. We attract and retain the best talent by investing in our employees and empowering them to develop themselves and their careers. Experience the challenges, rewards and opportunity of working for one of the world’s largest providers of products and services to the global energy industry.

Job Duties

We are seeking a highly skilled and motivated Senior AI Data Scientist to join our Subsurface team at our Abingdon office in Oxfordshire. This is a unique opportunity to apply advanced data science techniques to geological and geospatial challenges, helping us unlock insights from complex subsurface data.

Key Responsibilities
  • Collaborate with geoscientists and engineers to understand requirements and design effective solutions
  • Develop robust Python pipelines for data manipulation
  • Implement secure coding practices and manage version control using Git
  • Work with cloud platforms (AWS and Azure) to scale data workflows and manage infrastructure
  • Optimize database performance and spatial queries using PostgreSQL/PostGIS
  • Champion Python best practices across the team and support the development of junior team members
Required Qualifications
  • Honors degree (2:1 or above) in data science/AI or related field.
  • Minimum of 10 years related work experience.
Desirable Qualifications
  • Postgraduate qualification in AI or related field
Essential Skills
  • Proficiency in Python, with a strong adherence to Python best practices
  • Experience using Git for version control and collaboration
  • Knowledge of secure coding principles
  • Expertise in geospatial libraries such as GeoPandas, Shapely, and GDAL
  • Advanced knowledge of PostgreSQL/PostGIS for spatial data management
  • Experience with AWS and Azure platforms, including AI services (e.g., AWS SageMaker, Azure ML)
  • Proven experience developing or deploying AI models across domains such as natural language processing, computer vision, or predictive analytics
  • Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy)
  • Ability to design, train, and evaluate supervised and unsupervised learning algorithms
  • Strong teamwork and interpersonal skills, with a collaborative and agile mindset
  • Proven ability to work within agile development environments
  • Self-motivated, detail-oriented, and capable of managing multiple tasks
Desirable Skills
  • Knowledge of geological or subsurface data domains
  • Experience with containerization tools such as Docker and Kubernetes
  • Familiarity with CI/CD pipelines for automated deployment
  • Understanding of data governance and compliance in scientific environments
  • Experience with database virtualisation, including DecisionSpace integration server
  • Experience with data analysis applications from the Neftex Predictions portfolio

Halliburton is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation

Location

97 Jubilee Avenue, Milton Park,Abingdon,Oxfordshire,OX14 4RW, United Kingdom

Job Details

Requisition Number 204382
Experience LevelEntry-Level
Job FamilyEngineering/Science/Technology
Product Service Line[[division]]
Full Time / Part TimeFull-time

Additional Locations for this position

Compensation Information Compensatio

on is competitive and commensurate with experience.


#J-18808-Ljbffr

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.