Title: Data Analyst, QA
Sector: Vertical AI / SaaS
Institutional Funding: Secured
Investors: Leading European VC's and Tier-1 Angels
About Doczen
Doczen is a vertical-AI workflow automation platform tailored for the physical commodities sector. Embedded intocommunication channels, operating systems and other tools, Doczen acts as an automation engine and dynamic system ofrecord. Extracting and organizing unstructured data between workflows and departments, Doczen maximises collaboration,accountability and efficiency for physical commodities players across the entire supply chain.
The opportunity
We're looking for a detail-oriented Data Analyst, QA who thrives on ensuring data quality and validating AI workflows in acomplex industry. You'll be responsible for testing, benchmarking, and analyzing our RAG pipelines and document processing systems, ensuring they deliver accurate, reliable insights from messy commodity trading documents. You'll joina small, high-performing team at the stage where every decision still shapes the company, working within our secure development environment.
Tech you'll work with
- Python for data analysis and test automation
- PostgreSQL and Qdrant for data validation
- LangChain and RAG pipeline testing
- Our in-house benchmarking framework
- Cloud: Azure
- Data visualization and reporting tools
- Our focus is on accuracy, reliability, and security—ensuring our AI workflows deliver trustworthy results for mission-criticalcommodity trading decisions.
What you'll do
- Design and implement comprehensive testing strategies for RAG pipelines processing domain-specific commoditydocuments
- Develop and maintain our benchmarking framework to evaluate quality, latency, and cost of AI workflows
- Analyze data quality issues in unstructured document processing and work with engineers to resolve them
- Create automated test suites for data extraction, transformation, and loading processes
- Monitor and report on system performance, accuracy metrics, and data integrity
- Validate security and compliance requirements are met throughout data processing pipelines
- Collaborate with engineers and founders to establish quality standards and best practices
- Build dashboards and reports to track AI model performance and data quality metrics
- Participate in security awareness initiatives and ensure data handling meets compliance standards
You might be a fit if you...
- Have 3+ years of experience in data analysis, QA, or similar analytical roles
- Are proficient in Python for data analysis and test automation
- Have experience testing AI/ML systems, especially NLP or document processing workflows
- Understand vector databases, embeddings, and information retrieval concepts
- Excel at finding patterns in data and identifying edge cases others might miss
- Can create clear visualizations and reports to communicate complex findings
- Thrive in early-stage ambiguity: self-directed, detail-oriented, biased to action
- Understand the importance of data security and confidentiality in handling sensitive business information
- Prefer working from London or Madrid for easier face-to-face sessions—though we'll consider exceptional remote candidates who overlap UK/Spain hours
Nice-to-haves
- Experience with LLM evaluation frameworks and prompt testing
- Background in commodities, supply-chain, or other B2B domains with complex data
- Knowledge of data privacy regulations and compliance testing
- Experience with Azure cloud services
- Familiarity with security testing and compliance frameworks
Why join Doczen
- Early impact & ownership – shape how we ensure quality and reliability in a critical industry
- Mission & market – help solve painful, high-value problems for a trillion-dollar industry
-A-team peers – work shoulder-to-shoulder with excellent engineers building cutting-edge AI
- Competitive salary + equity – benefit directly from our growth
- Remote-friendly with strong asynchronous culture
- Secure environment – work with modern security practices and tools
- Information Security & Compliance
All team members are expected to maintain the confidentiality of company and client information, adhere to our informationsecurity policies, report security incidents promptly, and participate in security awareness training. We take data protectionseriously and expect all employees to handle sensitive commodity trading data, intellectual property, and system accesscredentials with appropriate care.
Hiring process
- 30-min intro call with CTO
- Technical assessment (data analysis case study or QA scenario)
- Take-home assignment focused on data quality analysis
- Founder interview & virtual coffee with wider team
- Offer
Ready to build with us? Email your GitHub/LinkedIn and a brief introduction on why you fit this role to nm@doczen.com