Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead the development of innovative machine learning algorithms and mentor fellow data scientists.
- Company: Join Shopify, a global leader in e-commerce empowering entrepreneurs since 2006.
- Benefits: Enjoy a digital-first work environment with opportunities for professional growth and impactful projects.
- Why this job: Make a real difference in commerce while thriving in a fast-paced, dynamic culture.
- Qualifications: Experience in data science, machine learning, and strong Python skills are essential.
- Other info: We value diverse perspectives and encourage applicants from all backgrounds to apply.
The predicted salary is between 43200 - 72000 £ per year.
Opportunity is not evenly distributed. Shopify puts independence within reach for anyone with a dream to start a business. Since 2006, we’ve grown to over 10,000 employees and generated over $500 billion in sales for millions of merchants in 175 countries. Every 28 seconds, an entrepreneur on Shopify makes their first sale. This is life-defining work that directly impacts people’s lives as much as it transforms your own.
About you
Moving at our pace brings a lot of change, complexity, and ambiguity—and a little bit of chaos. Shopifolk thrive on that and are comfortable being uncomfortable. That means Shopify is not the right place for everyone. Before you apply, consider if you can:
- Care deeply about what you do and about making commerce better for everyone
- Excel by seeking professional and personal hypergrowth
- Keep up with an unrelenting pace (the week, not the quarter)
- Be resilient and resourceful in face of ambiguity and thrive on (rather than endure) change
- Bring critical thought and opinion — and embrace differences and disagreement to get things done and move forward
- Work digital-first for your daily work
Data is a crucial part of Shopify’s mission to make commerce better for everyone. We organise and interpret petabytes of data to provide solutions for our merchants and stakeholders across the organisation. From pipelines and schema design to machine learning products and decision support, data science at Shopify is a diverse role with many opportunities to positively impact our success.
Our Staff (aka Lead) Data Scientists focus on pushing products and the business forward, with a focus on solving important problems rather than specific tools. We are looking for talented data scientists to help us better understand our merchants and buyers so we can help them on their journey. This role is focused on building the next generation Search algorithms, incorporating the newest ML techniques. You may also work on projects involving LLM-based work. You will also create the architecture for bigger projects and mentor senior data scientists to take on the challenge of building and coding your architecture into existence.
Experience required:
- Experience in the domain of search, personalisation, recommendations and machine learning techniques
- Commercial experience in a data science role solving high impact business problems
- Technical leadership - you are able to think about system design and architecture
- Mentoring senior data scientists
- Experience delivering business impact through data and machine learning products
- Experience building data pipelines and driving ETL design decisions
- Experience creating data product strategies, shipping data products, iterating after launch, and trying again
- Extensive experience using Python including a strong grasp of object oriented programming (OOP) fundamentals
- Strong ability to prioritise and communicate to technical and non-technical audiences alike
It would be great if you have:
- Built search related products e.g. chatbots
- Exposure to building data products that use generative AI and LLMs
- Previous experience using Spark (either via Scala or Pyspark)
- Experience with statistical methods like regression, GLMs or experiment design and analysis, shipping productionised machine learning systems or other advanced techniques are also welcome
All your information will be kept confidential according to EEO guidelines. We know that looking for a new role can be both exciting and time-consuming, and we truly appreciate your effort. And remember, we want to know what you’re really interested in building and why you want to build it at Shopify, so please give us as much detail on this as you’d like in the answers on the next page.
At Shopify, we understand that experience comes in many forms. We’re dedicated to adding new perspectives to the team - so if your experience is this close to what we’re looking for, please consider applying.
About the company
Shopify Inc. is a Canadian multinational e-commerce company headquartered in Ottawa, Ontario.
Staff Machine Learning Engineer employer: Shopify
Contact Detail:
Shopify Recruiting Team
accessibility@talentify.io
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Shopify's mission and values. Understanding their commitment to making commerce better for everyone will help you align your answers during interviews and demonstrate that you share their vision.
✨Tip Number 2
Showcase your experience with machine learning techniques relevant to search algorithms. Be prepared to discuss specific projects where you've successfully implemented these techniques, as this will highlight your ability to contribute to Shopify's goals.
✨Tip Number 3
Emphasise your technical leadership skills. Be ready to provide examples of how you've mentored others or led projects, as Shopify values candidates who can guide their teams through complex challenges.
✨Tip Number 4
Prepare to discuss your approach to ambiguity and change. Since Shopify thrives in a fast-paced environment, sharing experiences where you've adapted quickly and effectively will demonstrate your fit for their culture.
We think you need these skills to ace Staff Machine Learning Engineer
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Staff Machine Learning Engineer position at Shopify. Tailor your application to highlight how your skills and experiences align with their needs.
Showcase Relevant Experience: In your CV and cover letter, emphasise your experience in machine learning, data science, and any relevant projects you've worked on. Be specific about your contributions and the impact they had on previous employers.
Demonstrate Problem-Solving Skills: Shopify values critical thinking and problem-solving abilities. Use examples from your past work to illustrate how you've tackled complex challenges, especially in areas related to search algorithms or data product strategies.
Express Your Passion: In your application, convey your enthusiasm for making commerce better for everyone and your desire to contribute to Shopify's mission. Share what excites you about the role and how you envision making an impact.
How to prepare for a job interview at Shopify
✨Show Your Passion for Data
Make sure to express your enthusiasm for data science and how it can transform commerce. Share specific examples of how you've used data to solve problems in previous roles, especially in high-impact situations.
✨Demonstrate Technical Expertise
Be prepared to discuss your experience with machine learning techniques, particularly in search algorithms and data pipelines. Highlight your proficiency in Python and any relevant tools like Spark, as well as your understanding of system design and architecture.
✨Embrace Ambiguity
Since Shopify thrives in a fast-paced and sometimes chaotic environment, share experiences where you successfully navigated uncertainty. Discuss how you approach problem-solving when faced with ambiguity and how you adapt to change.
✨Communicate Effectively
Practice explaining complex technical concepts in simple terms. You’ll need to communicate with both technical and non-technical audiences, so demonstrate your ability to tailor your communication style to different stakeholders.
