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- Tasks: Create a proof of concept using data preparation and anomaly detection techniques.
- Company: Join a forward-thinking team focused on innovative machine learning solutions.
- Benefits: Remote work, flexible hours, and potential for contract extension.
- Why this job: Make a real impact by delivering clear insights from data to stakeholders.
- Qualifications: Strong Python skills and experience in feature engineering and anomaly detection.
- Other info: Great opportunity for hands-on experience in a dynamic, supportive environment.
The predicted salary is between 36000 - 60000 £ per year.
Duration: 5 weeks (with potential for extension)
Status: Outside IR35
Location: Remote (UK-based)
Start Date: ASAP
About the Role
We are seeking an experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders.
This is not a heavy Data Engineering or MLOps engagement. There is no requirement for live pipelines, streaming ingestion, or ongoing automated refresh. The successful candidate will work hands-on to profile data, engineer meaningful features, develop detection logic, and communicate findings effectively to non-technical stakeholders.
Essential Skills & Experience
- Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent)
- Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts
- Feature engineering: ability to craft interpretable, business-relevant features
- Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where appropriate
- Requirements & communication: ability to work with stakeholders, define success criteria, and explain outputs clearly
Desirable (Nice to Have)
- Power BI / BI visualisation (or similar) to support validation and stakeholder-facing outputs
- Familiarity with Azure for accessing datasets or sharing PoC artefacts (basic storage/compute)
- Ability to outline what would be needed to scale the PoC toward production later (without implementing full MLOps now)
Apply directly or contact mmatysik@trg-uk.com
Machine Learning Engineer in Edinburgh employer: trg.recruitment
Contact Detail:
trg.recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to your connections in the machine learning field and let them know you're on the lookout for opportunities. Sometimes, a friendly nudge can lead to a hidden gem of a job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data preparation and anomaly detection. 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 communication skills. Since you'll need to explain complex concepts to non-technical stakeholders, practice breaking down your work into simple terms that anyone can understand.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are eager to join us. Plus, it shows you're genuinely interested in being part of the StudySmarter team.
We think you need these skills to ace Machine Learning Engineer in Edinburgh
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, data processing, and feature engineering. We want to see how your skills match the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re the perfect fit for this Machine Learning Engineer role. We love seeing enthusiasm and a clear understanding of the project’s goals.
Showcase Your Anomaly Detection Skills: Since anomaly detection is key for this role, make sure to include specific examples of your experience with rule-based and statistical methods. We want to know how you’ve tackled similar challenges in the past!
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates!
How to prepare for a job interview at trg.recruitment
✨Know Your Tech Stack
Make sure you’re well-versed in Python and the libraries mentioned in the job description, like pandas and scikit-learn. Brush up on your data processing skills and be ready to discuss how you've used these tools in past projects.
✨Feature Engineering Focus
Since feature engineering is a key part of this role, prepare examples of how you've crafted business-relevant features from structured datasets. Be ready to explain your thought process and the impact of your features on model performance.
✨Communicate Clearly
You’ll need to explain complex concepts to non-technical stakeholders, so practice simplifying your explanations. Think about how you can present your findings in a clear and interpretable way, perhaps using visuals if you have experience with Power BI or similar tools.
✨Anomaly Detection Knowledge
Brush up on your anomaly detection techniques, both rule-based and statistical methods. Be prepared to discuss specific examples where you’ve implemented these methods and the outcomes they produced.
