Solutions Lead

About HASH HASH is building an open-source platform for structured knowledge and organizational decision-making. We turn information from databases, applications, documents, communications, sensors, and other sources into continuously updated knowledge and process graphs. From this shared model, organizations can analyze their operations, simulate possible futures, automate workflows, and give AI agents the context they need to act reliably. Our mission is to solve information failure and enable everybody to make the right decisions. We work on difficult technical and commercial problems, including applications in regulated and safety-critical environments. About the role We’re hiring a Solutions Lead to own that work across HASH’s enterprise pilots. You’ll operate at the boundary between customers, Sales, AI Success Engineers, researchers and product engineers. During sales and discovery, you will help determine what HASH should build. You’ll interview operators, executives and domain experts; study workflows, documents and data; identify the decisions and constraints that matter; and turn an initially ambiguous opportunity into a precise, valuable and buildable pilot. During delivery, you’ll define hypotheses, baselines, KPIs, acceptance criteria and evidence requirements before results exist. At the end, you’ll analyze what happened, state what the evidence does and does not support, and produce the substantive pilot report and case-study draft. This is a senior, hands-on role combining technical consulting, domain research, solution strategy and applied evaluation. You will variously support a sales conversation, run an expert interview, inspect data or sketch a process model, or write a methods section or executive report. Requirements Experience leading ambiguous technical or analytical engagements in which discovery changed the problem ultimately solved Excellent interviewing and facilitation skills, including the ability to surface tacit knowledge, exceptions, disagreement and actual decision criteria The ability to structure a domain in terms that experts and engineers both recognize as accurate and useful Technical fluency in data and AI, including the ability to inspect datasets with Python or SQL and identify system or model trade-offs Strong KPI judgment: measures should connect to the decision, be practical to collect, resist gaming and include appropriate guardrails Working knowledge of experimental design, causal inference and statistical uncertainty sufficient to design or critique an applied pilot evaluation Exceptional writing across implementation-ready specifications, academic methods, customer reports and concise executive conclusions Commercial awareness, coupled with the integrity to report uncertainty, limitations or negative results accurately High agency and comfort moving between customers, research and delivery without a complete brief Experience in technical consulting, AI…