Applied AI Engineer

• Investigate complex datasets using SQL, Snowflake, Python, and LLMs — directing agents to do the heavy lifting while you frame the question and judge the answer

• Unlock insights in large-scale B2B datasets that shape product direction and commercial strategy

• Build scalable, reusable datasets — and the skills that make them queryable by any agent or teammate

• Translate findings into recommendations that move metrics, not slides that summarise what happened

Data Quality & Source Management

• Own data quality end-to-end across rule-based and LLM-based checks, side by side — design the checks, run the evals, monitor drift, fix root causes

• Make the rule-vs-LLM judgement call on every check: deterministic logic where rules win, LLMs where semantic, contextual, or entity-resolution nuance is needed — and justify the split

• Assess and onboard new data sources: coverage, freshness, accuracy, and where LLM judges add lift over deterministic profiling

• Track down the hardest data bugs and fix them at the root, partnering with engineering and product

  • Scrape or source supplementary data when it sharpens insights or enriches the product