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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist (Product & Analytics) - **Company:** Small Parts, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Software as a Service, Data Warehousing, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Automation of Marketing, Operational Databases, Standard Sql, Git - **Published:** August 5, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/p9znfj4xgh ## About the Role 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 explain complex technical topics to non-technical stakeholders. * Have owned analytical products from idea through production. * Have experience working in B2C SaaS or product-led growth environments Our tech stack You'll work with technologies such as: * Python * SQL * dbt * Redshift * GrowthBook * AWS * Git * Modern BI and analytics tooling Experience with every tool isn't required - we value strong fundamentals over matching our exact stack. ## Description 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [The Innovation Formula: Fast Prototyping, Data Analysis, and Real User Insights](https://www.wearedevelopers.com/videos/1421-the-innovation-formula-fast-prototyping-data-analysis-and-real-user-insights) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)