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Machine Learning Engineer - Utilities

Help us use technology to make a big green dent in the universe!


Kraken powers some of the most innovative global developments in energy.


We’re a technology company focused on creating a smart, sustainable energy system. From optimising renewable generation, creating a more intelligent grid and enabling utilities to provide excellent customer experiences, our operating system for energy is transforming the industry around the world in a way that benefits everyone.


It’s a really exciting time in energy. Help us make a real impact on shaping a better, more sustainable future.


Kraken Customer


What we do: build the most AI-driven, innovative, forward-thinking platform for energy management. From optimizing resources to delivering cost-effective, exceptional customer experiences through advanced Customer Information Systems (CIS), billing, meter data management, CRM, and AI-driven communications, Kraken is powering the next wave of innovation in the energy industry. 


Why we do it: future energy will not look like energy as we know it today. We need to not just think about our future, but build for it. Now.


Our team


All our technology is written and maintained by a multi-discipline engineering team of around 1500 people globally. Our engineers work in ‘super teams’ which are focused on key areas of our platform as well as other innovative products. This also includes server-side, client-side and mobile engineers working closely with UX experts, copywriters and designers. 

 

Teams are empowered to choose a way of working that works for them, often opting for a Kanban-like approach. Notion and Asana are then used to specify and manage work; Github, CircleCI and Terraform Enterprise as part of an immutable-infrastructure, continuous delivery pipeline; and Datadog, Sentry and Cloudwatch to measure performance and monitor production. 


We’re now charging Kraken to power utilities beyond energy (think water, broadband,..) and have created a new business Kraken Utilities. We are building an innovation (ML/Data/AI) team from scratch and are looking for a Machine Learning Engineer to join and help us scale, implement a good culture, and work on some really exciting tech challenges. 

 

You'll join a talented team working on research around how to improve water efficiency (e.g. using smart meter data to detect leakages, reduce water consumption etc.); improve customer support efficiency (e.g. using LLM agents to automate actions and replies etc.).

 

Some of our coding conventions are open-source

 

Kraken is a great place to learn, work with some talented engineers and level-up your skills.  

 

Our technology


Our Data/ML stack includes: Python, SQL, pandas, Numpy, AWS, PyTorch, TensorFlow, NLP.


On the server-side, we mainly use Python. Most of our websites are powered by Django, Django-REST-framework and GraphQL (Graphene).  

 

We use AWS heavily  as part of a continuous deployment pipeline. See, for example, Django, ELB health checks and continuous delivery. 

 

Client-side, we use React, htmx and SASS; our mobile apps are built using native code or React Native.


What you'll do
  • Design, build, and deploy cutting-edge machine learning systems to address complex business challenges
  • Work collaboratively with cross-functional teams including product managers, software engineers, and other stakeholders to deliver exceptional ML products
  • Stay at the forefront of technology. Explore and evaluate new technologies to inspire the creation of new ML products and enhance existing ones
  • Conduct A/B experiments in collaboration with other teams, analyse results, and iterate to drive continuous improvement
  • Embrace ambiguity. As we scale, your role will evolve, and you should be comfortable adapting to changing responsibilities and focus areas.


What you'll need
  • You have a proven track record with 4+ years of hands-on experience applying machine learning to real-world business challenges within an industry setting
  • A solid foundation in the fundamentals of machine learning is a must. You are proficient in exploratory data analysis, model selection, model pipeline development, and the end-to-end process of model deployment and monitoring
  • Demonstrated expertise in Python, SQL, common ML, DL and visualisation libraries (pandas, numpy, scikit-learn, tensorflow, pytorch, huggingface, matplotlib etc.), ETL and data modelling
  • Experienced in cloud technologies, preferably with AWS
  • Strong communication skills are essential. You can articulate complex technical concepts to a diverse range of stakeholders with clarity
  • Proficient in software engineering fundamentals including version control and CI/CD pipelines
  • Nice to have: experience building NLP products; Kubernetes; dbt


Why else you'll love it here
  • Wondering what the salary for this role is? Just ask us! On a call with one of our recruiters it's something we always cover as we genuinely want to match your experience with the correct salary. The reason why we don't advertise is because we honestly have a degree of flexibility and would never want salary to be a reason why someone doesn't apply to Octopus - what's more important to us is finding the right octofit!
  • Octopus Energy is a unique culture. An organisation where people learn, decide, and build quicker. Where people work with autonomy, alongside a wide range of amazing co-owners, on projects that break new ground. We want your hard work to be rewarded with perks you actually care about! We won best company to work for in 2022, on Glassdoor we we're voted 50 best places to work in 2022 and our Group CEO, Greg has recorded a podcast about our culture and how we empower our people 
  • Visit our perks hub - Octopus Employee Benefits


This team will require candidates to work on a hybrid remote basis, coming into our office in Oxford Circus 2 days a week. You do also need to be able to work in the UK.


We're very excited to be growing our team. We're looking for skills and experience to help shape and define the future of not only our team, but the wider business at a global scale. If you're reading this and grinning, please apply! There are huge challenges to tackle, and we need amazing people who are keen to get stuck in.



If this sounds like you then we'd love to hear from you.


Are you ready for a career with us? We want to ensure you have all the tools and environment you need to unleash your potential. Need any specific accommodations? Whether you require specific accommodations or have a unique preference, let us know, and we'll do what we can to customise your interview process for comfort and maximum magic!


Studies have shown that some groups of people, like women, are less likely to apply to a role unless they meet 100% of the job requirements. Whoever you are, if you like one of our jobs, we encourage you to apply as you might just be the candidate we hire. Across Octopus, we're looking for genuinely decent people who are honest and empathetic. Our people are our strongest asset and the unique skills and perspectives people bring to the team are the driving force of our success. As an equal opportunity employer, we do not discriminate on the basis of any protected attribute. Our commitment is to provide equal opportunities, an inclusive work environment, and fairness for everyone.

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What You Should Know About Machine Learning Engineer - Utilities, Octopus Energy Group

Join Kraken as a Machine Learning Engineer and be part of a team that's making a substantial green impact in the utilities sector! Based in the vibrant city of London, UK, Kraken is pioneering innovations in the energy industry and aims to reshape systems for a sustainable future. In this role, you'll have the chance to design, build, and deploy cutting-edge machine learning systems that tackle real-world challenges like improving water efficiency and enhancing customer support. Imagine collaborating with cross-functional teams including product managers and software engineers, all committed to delivering exceptional results. With your background in machine learning, you'll dive deep into data, conduct A/B experiments, and stay at the forefront of emerging technologies. At Kraken, we believe in empowering our teams to choose their own working methods, often embracing a Kanban-like approach. You'll be part of a diverse and skilled multi-disciplinary engineering team where your contributions matter. If you're skilled in Python, SQL, and have experience with cloud technologies, we're looking for you! Come help us build a smarter, more sustainable energy system and make a real difference on a global scale. Ready to level up your skills and work with a team that celebrates creativity and innovation? Apply to be our next Machine Learning Engineer!

Frequently Asked Questions (FAQs) for Machine Learning Engineer - Utilities Role at Octopus Energy Group
What does a Machine Learning Engineer at Kraken do?

As a Machine Learning Engineer at Kraken, you will design, build, and deploy machine learning systems aimed at solving complex challenges in the utilities industry. This includes working on projects that improve water efficiency and enhance customer support. Your role will involve collaboration with cross-functional teams, data analysis, and exploring new technologies to drive innovation.

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What qualifications are needed to apply for the Machine Learning Engineer position at Kraken?

To be a successful candidate for the Machine Learning Engineer role at Kraken, you should have over 4 years of hands-on experience in applying machine learning to real-world business challenges. A solid understanding of machine learning fundamentals, along with proficiency in Python, SQL, and various ML libraries is crucial. Experience with AWS and strong communication skills will certainly boost your application.

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What programming languages and tools does Kraken use for machine learning projects?

Kraken utilizes a comprehensive tech stack for machine learning projects including Python, SQL, and libraries like pandas, NumPy, PyTorch, and TensorFlow. Familiarity with ETL, data modeling, and cloud technologies, especially AWS, is also vital for a Machine Learning Engineer at Kraken.

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How does Kraken foster innovation among its Machine Learning Engineers?

At Kraken, innovation is embedded in our culture. Machine Learning Engineers are encouraged to explore and evaluate new technologies, conduct A/B experiments, and adapt to the evolving needs of the business. Our engineering teams work collaboratively, allowing for the exchange of ideas and continuous improvement on ML products.

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What kind of team dynamics can I expect as a Machine Learning Engineer at Kraken?

As a Machine Learning Engineer at Kraken, you will be part of a diverse and talented multi-disciplinary team. The dynamic is collaborative and empowered, with teams choosing their own methods of working, often using Kanban-style approaches. You'll find yourself working closely with UX designers, software engineers, and product managers to bring innovative solutions to life.

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Is remote work an option for the Machine Learning Engineer position at Kraken?

Yes, the Machine Learning Engineer position at Kraken allows for a hybrid work model. You will be expected to come into the office in Oxford Circus two days a week, providing a balance between collaborative in-person work and the flexibility of remote work.

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What can I expect from Kraken's company culture as a Machine Learning Engineer?

Kraken boasts a unique culture where autonomy, rapid decision-making, and innovation are at the forefront. Our teams are encouraged to learn, adapt, and build projects that break new ground. Moreover, Kraken has been recognized as one of the best companies to work for, fostering an inclusive and supportive workplace environment.

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Common Interview Questions for Machine Learning Engineer - Utilities
Can you describe a machine learning project you worked on and the impact it had?

In responding to this question, provide details about a specific machine learning project, the challenges faced, the methodologies used, and the outcomes. Demonstrate how your work contributed value to the business or a client's needs, highlighting metrics where possible.

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How do you approach model selection and evaluation in machine learning projects?

Discuss your process for selecting machine learning models based on the problem statement and dataset. Explain the evaluation metrics you prefer to use for different types of models, and how these guide your decisions about model tuning and optimization.

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What are some common pitfalls in deploying machine learning models and how do you avoid them?

Highlight typical challenges faced during model deployment, such as data drift or version control issues. Share your strategies for monitoring model performance post-deployment and ensuring ongoing efficacy through regular updates and retraining as needed.

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Explain your experience with Python and its libraries for machine learning.

Detail your proficiency in Python, including specific libraries like scikit-learn, TensorFlow, or PyTorch. Expand on how you’ve applied these tools in real-world projects and how they helped facilitate data manipulation, model training, and deployment.

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How do you handle ambiguity in project requirements or data?

Demonstrate your adaptability by explaining your approach to navigating ambiguous project specifications. Discuss any techniques you employ to clarify requirements, gather stakeholder feedback, and iteratively refine your project goals.

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What experience do you have with A/B testing in machine learning applications?

Elaborate on your experience conducting A/B tests, discussing objectives, your methodology for designing tests, and how you analyze and interpret the results to guide decision-making. Provide examples where possible.

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Can you discuss your experience with cloud technologies, particularly AWS?

Share your familiarity with AWS services such as SageMaker, EC2, or S3, focusing on how you utilize these tools to support machine learning workflows. Highlight specific projects where AWS was integral to your implementation.

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What strategies do you use for communicating complex technical concepts to non-technical stakeholders?

Discuss your communication style and techniques you use to simplify and clarify complex machine learning concepts, helping non-technical stakeholders understand the implications of the technology being applied.

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Describe a time you worked in a cross-functional team on a machine learning project.

Reflect on your experience collaborating with teams outside of the ML domain, such as product management or design teams. Describe the project’s objectives, your role, and how effective collaboration contributed to successful outcomes.

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How do you stay up-to-date with advancements in machine learning technologies?

Discuss your commitment to continuous learning through online courses, attending conferences, participating in relevant forums, and engaging with recent publications in machine learning. Share any specific resources or communities you find valuable.

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Octopus Energy is a forward-thinking energy provider - were not just thinking about us, but generations ahead. Were reinventing the way you interact with your energy use so that you know when the best times are to use energy, and when the grid is ...

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Full-time, hybrid
DATE POSTED
November 25, 2024

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