Senior Data Scientist - Pexels

Canva
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Join to apply for the Senior Data Scientist - Pexels role at Canva

Join to apply for the Senior Data Scientist - Pexels role at Canva

Get AI-powered advice on this job and more exclusive features.

Job Description

Job Description

Join the team redefining how the world experiences design.

Hiya, g'day, mabuhay, kia ora, 你好, hallo, vítejte!

Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point.

Where And How You Can Work

The buzzing Canva London campus features several buildings around beautiful leafy Hoxton Square in Shoreditch. While our global headquarters is in Sydney, Australia, London is our HQ for Europe, with all kinds of teams based here, plus event spaces to gather our team and communities. You'll experience a warm welcome from our Vibe team at front of house, amazing home cooked food from our Head Chef and a variety of workspaces to hang out with your team mates or get solo work done. That said, we trust our Canvanauts to choose the balance that empowers them and their team to achieve their goals and so you have choice in where and how you work.

What You’d Be Doing In This Role

As Canva scales change continues to be part of our DNA. But we like to think that's all part of the fun. So this will give you the flavour of the type of things you'll be working on when you start, but this will likely evolve.

At The Moment, This Role Is Focused On

  • Find insights that help us understand both Pexels and our customers - we want you to be creative and insightful, rather than just throwing the usual metrics at the wall to see what sticks.
  • Suggest and run interesting experiments that will actually move our metrics - when we push tests to 100% we want to actually see the bump in the relevant graph.
  • Build and maintain dashboards for our product teams, and make them look really good - you don’t need to be a designer or have any design talent, you just need to want to build cool things!
  • Own projects, data models, dashboards, and other artefacts and keep them up-to-date and high quality in a hypergrowth environment.
  • Use our giant databases to answer the sort of questions that sound really simple but turn out to be challenging (and fun!).
  • Help us extend data literacy and curiosity throughout the whole company.


You're probably a match if

  • You have strong competency with SQL. Experience with data warehouses such as Snowflake, Redshift, or BigQuery is a plus!
  • You are experienced with data wrangling and analyzing data using Python or R.
  • You can demonstrate skills across optimising product funnels, running experiments, and building dashboards that effectively communicate results and outcomes.
  • You may have a grounding in mathematics/statistics and a bachelor's degree in a STEM area (not mandatory).
  • You may have played with very large data sets (not mandatory).
  • You are a strong communicator, verbally and written, with the ability to break down complex topics into simple solutions and ideas.


About The Team

We build Pexels, a free and open platform for real-world visuals. Our team connects hundreds of thousands of creators with millions of storytellers, making authentic photos and videos easy to find and safe for commercial use. We focus on community, quality, and real impact.

We’re looking for a data scientist who is eager to help Pexels forge ahead into a new product space by helping to shape product decisions through actionable insights, experimentation, and reporting.

You’ll work closely with our product, engineering, community and marketing teams to uncover the right questions to ask and bring strategic thinking to every decision. You’ll be part of a dedicated, cross-functional squad that supports you in doing your best work. Your coach will guide your personal and professional growth so you can focus on what you love and keep getting better at it.

  • Ship features that help creators track views, downloads, and reach.
  • Run challenges and meetups that surface new talent from everywhere.
  • Curate and grow a diverse library that updates daily.
  • Work in small, autonomous squads with clear goals and a bias to ship.


Join us to put human creativity at the centre of how the world sees itself.

What's in it for you?

Achieving our crazy big goals motivates us to work hard - and we do - but you'll experience lots of moments of magic, connectivity and fun woven throughout life at Canva, too. We also offer a range of benefits to set you up for every success in and outside of work.

Here's a Taste Of What's On Offer

  • Equity packages - we want our success to be yours too
  • Inclusive parental leave policy that supports all parents & carers
  • An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more
  • Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally


Check out lifeatcanva.com for more info.

Other Stuff To Know

We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.

We celebrate all types of skills and backgrounds at Canva so even if you don’t feel like your skills quite match what’s listed above - we still want to hear from you!

Please note that interviews are conducted virtually.Seniority level

  • Seniority levelMid-Senior level

Employment type

  • Employment typeFull-time

Job function

  • Job functionInformation Technology
  • IndustriesSoftware Development

Referrals increase your chances of interviewing at Canva by 2x

Sign in to set job alerts for “Senior Data Scientist” roles.

Gatwick, England, United Kingdom 2 weeks ago

London, England, United Kingdom 5 days ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 4 days ago

London, England, United Kingdom 5 days ago

London, England, United Kingdom 1 week ago

London, England, United Kingdom 6 days ago

London, England, United Kingdom 1 week ago

London, England, United Kingdom 1 day ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 1 week ago

London, England, United Kingdom 4 days ago

London, England, United Kingdom 2 weeks ago

London Area, United Kingdom £50,000.00-£60,000.00 15 hours ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 20 hours ago

London, England, United Kingdom 6 days ago

London, England, United Kingdom 2 days ago

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.