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About the Role
Solutions Lead to own enterprise pilot work, operating at the boundary between customers, Sales, AI Success Engineers, researchers, and product engineers. This senior, hands-on role combines technical consulting, domain research, solution strategy, and applied evaluation.
Responsibilities
- Help determine what HASH should build during sales and discovery by interviewing operators, executives, and domain experts.
- Study workflows, documents, and data to identify key decisions and constraints.
- Turn ambiguous opportunities into precise, valuable, and buildable pilots.
- Define hypotheses, baselines, KPIs, acceptance criteria, and evidence requirements for pilots.
- Analyze pilot results, state what evidence supports or does not support conclusions, and produce pilot reports and case-study drafts.
- Support sales conversations, run expert interviews, inspect data, sketch process models, or write reports.
- Join important customer conversations and help Sales distinguish interesting problems from valuable, feasible, and provable opportunities.
- Plan and facilitate discovery workshops with stakeholders.
- Conduct expert interviews to surface how a system actually works, including exceptions and uncertainty.
- Synthesize interviews, process documents, and data into structured domain models.
- Translate customer objectives into clear product, data, model, and workflow requirements.
- Define focused pilots with explicit hypotheses, scope, responsibilities, success criteria, and routes to wider deployment.
- Build KPI trees linking technical performance, user behavior, operational change, and financial value.
- Establish baselines and design credible evaluations.
- Ensure required evidence is instrumented and collected during delivery.
- Analyze pilot results, uncertainty, limitations, safety behavior, and practical significance.
- Write rigorous pilot reports and the first substantive draft of customer case studies.
- Work with Marketing to turn validated evidence into clear public communication.
- Coordinate external academics or evaluators for independent validation.
- Capture reusable patterns to improve future discovery, domain modeling, and evaluation.
- Own pilot delivery into a customer once an engagement is secured.
Requirements
- Experience leading ambiguous technical or analytical engagements where discovery changed the problem solved.
- Excellent interviewing and facilitation skills to surface tacit knowledge, exceptions, disagreement, and actual decision criteria.
- Ability to structure a domain in terms that experts and engineers both recognize as accurate and useful.
- Technical fluency in data and AI, including inspecting datasets with Python or SQL and identifying 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 skills across implementation-ready specifications, academic methods, customer reports, and concise executive conclusions.
- Commercial awareness 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 transformation, operations research, analytics, digital twins, process mining, knowledge graphs, simulation, or decision science is particularly relevant.
- Work experience in supply chains, manufacturing, life sciences, chemicals, logistics, energy, infrastructure, or other complex domains is relevant.
- Excellent written and spoken English is essential.
- German or another European language is valuable.
- Travel to customer sites will sometimes be required.
Skills
- Python
- SQL
- Data Analysis
- AI
- Experimental Design
- Causal Inference
- Statistical Uncertainty
- KPI Development
- Domain Modeling
- Technical Consulting
- Process Mining
- Knowledge Graphs
- Simulation
- Decision Science
- Supply Chain Management
- Manufacturing
- Life Sciences
- Chemicals
- Logistics
- Energy
- Infrastructure
Location
- London
Work Type
- Full-time
- Hybrid
Experience Level
- Senior
Education Level
- Advanced quantitative, scientific or systems degree is useful but not required.
Salary/Compensations
- £100,000-140,000
Benefits
- Generous equity/bonus offered in addition to base salary.
About the Company
- 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.
- We've raised $5.5m+ from Silicon Valley VCs, and have contracted >$10m in revenue in the last 18 months.
- Our founding team have established and sold companies for tens of millions, hundreds of millions, and billions of dollars (including household names like Trello and Stack Overflow).
- Our platform is differentiated, open-source infrastructure rather than a thin wrapper around a commodity product.
- Work directly with the founder, customers and a deeply technical product and research team.
- Join at a moment of rapid growth, with outsized scope and influence.
- Be part of a high-agency team that cares about output, ownership and quality.