Data Scientist: Product & Analytics
Smallpdf
| Company | Smallpdf |
| Category | Data & Analytics |
| Location | Remote |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 7 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
ABOUT SMALLPDF
Smallpdf was founded in 2013 with one simple belief: working with documents shouldn't be complicated.
What started as an easier way to compress PDFs has grown into one of Europe's largest product-led technology companies. Today, more than 17 million people use Smallpdf every month, our products have been used by over 1.1 billion users across 195 countries, and every day we power millions of document workflows - from PDF conversion and editing to e-signing and AI-powered document tools.
Our success came from rethinking an established market. Instead of building complex software for experts, we broke document management down into simple jobs to be done - compress a PDF, merge files, convert documents, collect signatures - and made those experiences intuitive for everyone. That product philosophy continues to shape how we build today.
For a Data Scientist, this creates an unusually interesting environment.
Every month, millions of users interact with our products across acquisition channels, pricing, subscriptions, experimentation, product adoption, search, and AI-powered experiences. Every feature launch, monetization decision, and product improvement generates signals at a scale that allows us to test ideas, validate hypotheses, and continuously improve the user experience through data.
ABOUT THE ROLE
This isn't a traditional Data Scientist role. You won't spend your days optimizing machine learning models in notebooks or publishing research. Instead, you'll own analytical products that directly influence how millions of users experience Smallpdf.
You'll work across Product, Growth and Engineering to:
- design experiments
- improve decision making
- own production analytical systems
- build reliable data products
- uncover opportunities hidden in data
- translate complex analyses into business impact
You'll be joining a small, highly autonomous team where everyone owns outcomes rather than tasks.
If you're looking for a role where you can combine engineering, statistics, product thinking and business impact, you'll enjoy this.
WHAT YOU'LL DO
Own production data products
You'll maintain and evolve analytical systems that directly power business decisions, including our marketing automation pipeline and core semantic data models.
Design and analyze experiments
Work closely with Product and Growth teams to design experiments, define success metrics, analyze outcomes and recommend next steps.
Build reliable analytical foundations
Develop dbt models, improve data quality, monitor production pipelines and ensure stakeholders trust the data they use every day.
Solve ambiguous business problems
You'll rarely receive perfectly defined tasks. Instead, you'll partner with stakeholders to understand problems, explore data, identify opportunities and propose solutions before anyone asks.
Turn data into decisions
Communicate insights clearly to technical and non-technical audiences, helping teams prioritize what matters most.
Continuously improve our data platform
You'll work with Data Engineers to improve our analytical infrastructure, semantic layer, experimentation framework, as well as future AI evaluation systems.
WHAT WE'RE LOOKING FOR
You probably have
- Several years of experience as a Data Scientist in a modern product company.
- Strong Python and SQL skills.
- Experience building production data products that drove real business impact - not just notebooks.
- Solid understanding of experimentation, statistics and hypothesis testing.
- Experience working with modern data warehouses and transformation tools (dbt is a plus, not a requirement).
- Experience partnering directly with Product, Growth or Marketing teams.
YOU'LL STAND OUT IF YOU
- Love understanding products and users.
- Naturally investigate anomalies instead of ignoring them.
- Enjoy solving messy, ambiguous problems.
- Care about shipping useful work rather than perfect work.
- Can expla
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