Data Product Analyst, Private Investor

About the role

We are looking for a Data Product Analyst to join the Signals team and help us hit our ambitious 2026 goals. This is not a role where you'll be a cog in a machine. On a small team building a product at a rapid pace, you'll operate across many surfaces: vetting new data sources, running customer-facing analysis, building product feedback loops, and designing pipeline refactors to improve accuracy.

Responsibilities

• Become a core owner of Signals data products – you will help prioritize and vet new data sources and design their incorporation into our pipelines and platform
• Work closely with our AI and Data Engineering teams to build and maintain cutting-edge AI-tagging systems
• Design, maintain, and run QA processes to validate data across hundreds of thousands of vendors, proactively catching issues before they reach clients
• Investigate and resolve complex data quality issues independently. On a small team, there's no one to hand things off to; you own the problem through to resolution
• Serve as a trusted resource for internal and external stakeholders by deeply understanding client use cases and aligning them with our data methodologies, coverage, and limitations
• Conduct and deliver detailed analytical write-ups on specific vendors, sectors, or trends that showcase the power of Signals data and drive client engagement
• Collaborate cross-functionally with Engineering, Product, and Go-to-Market to ship improvements fast—whether that's adding new data to the platform, improving accuracy, or reducing data lag times
• Help shape the product roadmap by translating customer feedback and data quality insights into actionable improvements
• Proactively identify opportunities to improve efficiency, robustness, and data quality within team workflows and build AI automation where possible

Requirements

• 4–6 years of experience in data analytics, data operations, consulting, or a related field
• 4+ years of experience working with SQL, Python, Pyspark, or other programming languages to explore and transform large, complex datasets, and you're familiar with GitHub and Databricks
• Experience using sample or panel data to make precise estimates that guide decision-making
• Proactive experimentation with AI tools to increase speed, depth, and efficiency of analysis, and to unlock new approaches to problem-solving
• Scrapiness and resourcefulness—you figure things out without waiting to be told what to do
• Adaptability in ambiguous and fast-paced environments
• Excellent attention to detail and a natural instinct for spotting data anomalies
• Clear and concise communication—both in writing and verbally
• Intellectual curiosity about technology markets, business trends, and care about how data is used to influence decision-making
• Ownership of problems and driving them to completion without constant oversight