Employer description & source details
Define the AI data strategy, across products and customer types Drive every data purchase decision, approving orders that meet the bar on cost, customer needs and model fit. Evaluate sourcing options with AI Data Ops, such as archive vs. new capture, satellite vs. aerial, vendors and timing, and recommend the best fit. Lead monthly quote-to-actual analysis, identify root causes of variance, and drive revisions to cost bands and decision rules. Build multi-year data plans and the unit-cost models behind data pricing. Partner with Data Science and Engineering to define requirements for production tooling, and drive adoption across user teams. Design and prototype the agentic workflow by turning guidelines into machine-readable rules and piloting AI-assisted steps. You'll work with Customer Success and engagement managers, AI Data Ops, Data Science and R&D, Partnerships, Finance, Legal, and Sales and Product. You will draw on their inputs, including customer scope, vendor quotes, model requirements, contract terms, and actual costs, and provide clear order decisions, cost bands, forecasts, and root-cause analysis in return. You’ll work closely with the leadership to define the strategic direction for data in Aidash. MBA plus 5 to 6 years in management or analytics consulting, strategy and operations, procurement or sourcing strategy, or a commercial finance role at a B2B or SaaS company. Strong cost and commercial analysis. You can build a unit-cost model from scratch, take apart someone else's, and explain a variance to its root cause. Advanced proficiency in Excel or Google Sheets and working knowledge of SQL. Demonstrated experience designing decision rules, thresholds, or approval workflows adopted by other teams. Regular use of AI tools, with at least one workflow aut
Extracted skills: python, excel, sales, customer success, finance, operations