At a Glance
- Tasks: Build and deploy scalable ML pipelines for real-time energy data processing.
- Company: Join a vibrant tech company at the forefront of energy innovation.
- Benefits: Flexible hybrid working, private health insurance, and equity options.
- Why this job: Make a real impact on energy systems with cutting-edge ML technologies.
- Qualifications: Experience in ML engineering, Python, and deploying models in production.
- Other info: Collaborative environment with opportunities for continuous learning and growth.
The predicted salary is between 36000 - 60000 £ per year.
Processing thousands of energy data points per second from diverse operational sources, handling massive volumes of energy data while running sophisticated classification and anomaly detection models in real-time, maintaining comprehensive data lineage, and delivering insights through high-performance platforms used by energy operators globally requires exceptional engineering and scientific expertise. This processing demands models that can withstand the scrutiny of energy analysts and traders, operations teams, and regulatory bodies, with the performance, stability, and reliability that critical energy systems require.
The Data Platform Team is responsible for all machine learning operations across our energy data ecosystem. We work with everything from raw sensor data from millions of energy assets to complex operational datasets, generating high-value predictions such as equipment failure detection, energy demand forecasting, operational anomaly identification, predictive maintenance scheduling, and system optimization recommendations. The team has built a comprehensive suite of statistical and machine learning models that enable us to provide the most accurate and actionable insights into energy operations. We take pride in applying cutting-edge research to real-world energy challenges in a robust, scalable, and maintainable way. The quality of our models is continuously validated by experienced in-house energy analysts and traders and domain experts to ensure reliability of our predictions.
You will be instrumental in designing and building ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models. Working with software engineers, data scientists, and energy analysts and traders, you will help bridge the gap between research experiments and production energy systems by ensuring 100% uptime and bulletproof fault-tolerance of every component of our ML platform.
Requirements
- Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow.
- With solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost.
- Skilled in developing classification models and anomaly detection systems for production environments.
- Capable of implementing comprehensive data lineage tracking and model governance systems.
- Driven by working in an intellectually engaging environment with top energy analysts and traders and technology experts, where constructive challenges and technical debates are encouraged.
- Excited about working in a dynamic environment: not afraid of complex energy challenges, eager to bring new ML innovations to production, and a positive can-do attitude.
- Passionate about mentoring team members, helping them improve their ML engineering skills and grow their careers.
- Experienced with the full ML model lifecycle, including experiment design, model development, validation, deployment, monitoring, and maintenance.
Awesome If You:
- Have experience in the energy sector or understanding of energy systems and operations.
- Have practical experience with AWS services (SageMaker, S3, EC2, Lambda, etc.).
- Have experience with infrastructure as code tools (Terraform, CloudFormation).
- Have experience with Apache Kafka and real-time streaming frameworks.
- Are familiar with observability principles such as logging, monitoring, and distributed tracing for ML systems.
- Have experience with transformer architectures and generative AI applications in operational contexts.
- Have experience with time series analysis and forecasting techniques relevant to energy applications.
- Are knowledgeable about data privacy regulations and compliance frameworks in the energy sector.
Benefits
- Enjoy flexible hybrid working – split your time between home and our office, with the freedom to work where you’re most productive.
- A vibrant, diverse company pushing ourselves and the technology to deliver beyond the cutting edge.
- A team of motivated characters and top minds striving to be the best at what we do at all times.
- Constantly learning and exploring new tools and technologies.
- Acting as company owners (all Vortexa staff have equity options)– in a business-savvy and responsible way.
- Motivated by being collaborative, working and achieving together.
- Private Health Insurance offered via Vitality to help you look after your physical health.
- Global Volunteering Policy to help you ‘do good’ and feel better.
ML Engineer / Data Scientist (Hiring Immediately) in London employer: Vortexa
Contact Detail:
Vortexa Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer / Data Scientist (Hiring Immediately) in London
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or attend industry meetups. We can’t stress enough how personal connections can give you the inside scoop on job openings and company culture.
✨Tip Number 2
Prepare for technical interviews by brushing up on your ML fundamentals. We recommend practising coding challenges and discussing your past projects. Show them you’re not just a theory whiz but can apply your skills in real-world scenarios!
✨Tip Number 3
Don’t forget to showcase your passion for energy systems! When you get the chance, share your thoughts on recent trends or innovations in the sector. We love seeing candidates who are genuinely excited about the field.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining our team and contributing to our mission in the energy sector.
We think you need these skills to ace ML Engineer / Data Scientist (Hiring Immediately) in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match our job description. Highlight your experience with ML pipelines, Python, and any relevant projects you've worked on in the energy sector.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this role and how your background makes you a great fit. Share specific examples of your work with classification models or anomaly detection systems.
Showcase Your Passion for Energy: We love candidates who are passionate about the energy sector! Mention any relevant experiences or insights you have about energy systems and how they relate to your work in machine learning.
Apply Through Our Website: For the best chance of getting noticed, apply directly through our website. It helps us keep track of applications and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at Vortexa
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, PyTorch, and Kubernetes. Brush up on your experience with ML pipelines and be ready to discuss specific projects where you've implemented these tools.
✨Showcase Your Problem-Solving Skills
Prepare to discuss complex energy challenges you've faced in previous roles. Think of examples where you developed classification models or anomaly detection systems, and be ready to explain your thought process and the impact of your solutions.
✨Understand the Energy Sector
Familiarise yourself with the energy sector and its operational challenges. Being able to speak knowledgeably about energy systems and data privacy regulations will show that you’re not just a tech whiz but also understand the context in which you'll be working.
✨Be Ready to Collaborate
This role involves working closely with software engineers, data scientists, and energy analysts. Prepare to discuss how you’ve successfully collaborated in the past, and think about how you can contribute to a team environment that values constructive challenges and technical debates.