Solutions Architect

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 (or a Solutions Architect) 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 transformation, operations research, analytics, digital twins, process mining, knowledge graphs, simulation or decision science is particularly relevant. So is work in supply chains, manufacturing, life sciences, chemicals, logistics, energy, infrastructure or other complex domains.
• An advanced quantitative, scientific or systems degree is useful but not required.
• Excellent written and spoken English is essential. German or another European language is valuable.
• Travel to customer sites will sometimes be required.

What you'll do

• Join important customer conversations and help Sales distinguish interesting problems from valuable, feasible and provable opportunities
• Plan and facilitate discovery workshops with operational, technical and executive stakeholders
• Conduct expert interviews that surface how a system actually works, including its exceptions and uncertainty
• Synthesize interviews, process documents and data into structured domain models: entities, states, events, relationships, actions, constraints, objectives and outcomes
• Translate customer objectives into clear product, data, model and workflow requirements for engineers and researchers
• 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 using experimental, quasi‑experimental, replay, simulation or observational methods as appropriate
• Make sure the required evidence is instrumented and collected during delivery rather than reconstructed afterwards
• 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 without overstating the result
• Coordinate external academics or evaluators when genuinely independent validation is required
• Capture reusable patterns so that future discovery, domain modelling and evaluation become faster and better

In short, you'll be intimately involved in scoping solutions for sale, and once an engagement has been secured, you'll own pilot delivery into that customer.

Why HASH

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. Shape what gets built, how pilots are run and what the company can honestly claim afterwards. 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.

A base salary will be offered of £100,000-140,000 in London. Compensation in this role is extremely heavily performance‑based, with generous equity/bonus offered in addition to the base salary, in order to align sales incentives.