At a Glance
- Tasks: Design and build ML infrastructure for real-time energy data processing and insights.
- Company: Vibrant tech company focused on innovative energy solutions.
- Benefits: Flexible hybrid working, private health insurance, and equity options.
- Why this job: Join a dynamic team tackling complex energy challenges with cutting-edge ML technology.
- Qualifications: Experience in ML pipelines, Python, and energy systems; passion for mentoring.
- Other info: Collaborative environment with opportunities for continuous learning and growth.
The predicted salary is between 36000 - 60000 Β£ 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.
You Are:
- 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.
Enjoy flexible hybrid working - split your time between home and our office, with the freedom to work where you are 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 βdo good' and feel better.
ML Engineer / Data Scientist employer: Vortexa
Contact Detail:
Vortexa Recruiting Team
StudySmarter Expert Advice π€«
We think this is how you could land ML Engineer / Data Scientist
β¨Tip Number 1
Network like a pro! Reach out to folks in the energy sector on LinkedIn or at industry events. A friendly chat can lead to opportunities that arenβt even advertised yet.
β¨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to energy data. This gives potential employers a taste of what you can do and sets you apart from the crowd.
β¨Tip Number 3
Prepare for technical interviews by brushing up on your Python, PyTorch, and XGBoost skills. Practice coding challenges and be ready to discuss your past projects in detailβthis is your chance to shine!
β¨Tip Number 4
Donβt forget to apply through our website! Itβs the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace ML Engineer / Data Scientist
Some tips for your application π«‘
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the 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. Share specific examples of how you've tackled complex challenges in ML or energy data, and show us your passion for innovation.
Showcase Your Projects: If you have any personal or professional projects related to ML or energy systems, donβt hesitate to include them. We love seeing practical applications of your skills, especially if they demonstrate your problem-solving abilities.
Apply Through Our Website: We encourage you to apply directly through our website. Itβs the best way for us to receive your application and ensures youβre considered for the role. Plus, it shows us youβre keen to join our team!
How to prepare for a job interview at Vortexa
β¨Know Your Tech Inside Out
Make sure youβre 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.
β¨Showcase Your Problem-Solving Skills
Prepare to discuss real-world energy challenges you've tackled. 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.
β¨Understand the Energy Sector
Familiarise yourself with the energy sector's nuances, including data privacy regulations and compliance frameworks. Being able to speak knowledgeably about energy systems will show that youβre not just a tech whiz but also understand the industry context.
β¨Be Ready for Technical Discussions
Expect technical debates during the interview. Prepare to discuss your approach to model governance, data lineage tracking, and how you ensure uptime and fault tolerance in ML systems. This is your chance to shine as a collaborative team player!