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- Tasks: Analyse and refine forecasting models for the European gas market using Python.
- Company: Join a leading global financial information and research organisation.
- Benefits: Competitive pay of ÂŁ175/day with holiday pay, sick pay, and pension included.
- Why this job: Make an impact in energy analytics while developing your data skills in a dynamic environment.
- Qualifications: Strong Python skills and experience with complex datasets are essential.
- Other info: No prior gas-market experience needed; perfect for data-focused analysts looking to grow.
The predicted salary is between 140 - 210 ÂŁ per hour.
London‑based (City location) 4 days onsite / 1 day from home £175/day via PAYE (holiday pay, sick pay & pension included). We are supporting our client, a global market‑leading financial information & research organisation, known for its high‑calibre analytics teams and advanced data environment. They are expanding their European energy analytics capability and require a data‑driven analyst to support their gas‑market research function during a maternity cover period.
This role sits within a specialist European gas analytics group, contributing to the modelling, scripting and data‑quality foundations behind their energy‑market insights. Although the team covers gas‑market fundamentals, previous experience in the gas or LNG sector is not mandatory but rather desirable. What matters most is strong Python capability, comfort with complex datasets, and the ability to collaborate with analysts to improve forecasting tools and workflows.
This is a highly hands‑on position where you’ll refine models, enhance scripts, test data processes, and help shape analytical outputs in a fast‑paced, intellectually curious environment.
Essential Skills- Strong Python coding experience, including testing, debugging and improving existing scripts.
- Ability to work confidently with large, multi‑source datasets — cleaning, validating and structuring data.
- Familiarity with statistical or time‑series techniques (forecasting, regressions, pattern identification).
- Strong analytical mindset, able to challenge assumptions and extract meaningful insights from noisy data.
- Clear communication skills and the ability to work closely with subject‑matter experts.
- Excellent problem‑solving ability and a proactive, curious approach.
- No prior gas‑market or energy‑sector experience required — open to analysts from any data‑focused background.
- Maintain, refine and run forecasting models for European gas‑market fundamentals.
- Improve existing Python scripts and develop new ones to support analytical workflows.
- Ensure data quality, structure and consistency across various datasets used in forecasting.
- Provide quantitative insight and technical input to ongoing analytical work.
- Assist with process mapping, documentation and continuous improvement initiatives.
- Conduct rigorous testing, debugging and optimisation of analytical tools and scripts.
European Gas Data Analyst employer: Nicoll Curtin Technology
Contact Detail:
Nicoll Curtin Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land European Gas Data Analyst
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, especially those involving data analysis. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the gas market basics. Practice common interview questions and be ready to discuss how you've tackled complex datasets in the past.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace European Gas Data Analyst
Some tips for your application 🫡
Show Off Your Python Skills: Make sure to highlight your Python coding experience in your application. We want to see how you've used it to tackle complex datasets and improve scripts. Don’t hold back on sharing specific examples!
Data Quality is Key: Since this role involves working with large datasets, emphasise your ability to clean, validate, and structure data. We love seeing candidates who can ensure data quality and consistency, so give us the details!
Be Analytical and Curious: We’re looking for someone with a strong analytical mindset. In your application, share how you’ve challenged assumptions and extracted insights from data. Show us your problem-solving skills and proactive approach!
Communicate Clearly: Clear communication is essential for collaborating with our team. Make sure your application reflects your ability to convey complex ideas simply. We want to know how you’ve worked with others to achieve great results!
How to prepare for a job interview at Nicoll Curtin Technology
✨Know Your Python Inside Out
Since strong Python coding experience is essential for this role, make sure you brush up on your skills. Be prepared to discuss specific projects where you've tested, debugged, or improved scripts. Practising coding challenges can also help you feel more confident.
✨Get Comfortable with Data
This position involves working with large, multi-source datasets, so be ready to talk about your experience in cleaning, validating, and structuring data. Think of examples where you've tackled complex datasets and how you ensured their quality and consistency.
✨Show Off Your Analytical Mindset
The role requires a strong analytical mindset, so come prepared with examples of how you've challenged assumptions and extracted insights from noisy data. Discuss any statistical or time-series techniques you're familiar with, as this will demonstrate your capability in forecasting.
✨Communicate Clearly and Collaborate
Clear communication is key, especially when working with subject-matter experts. Practice explaining complex concepts in simple terms and think of instances where you've successfully collaborated with others to improve workflows or analytical outputs.