Head of Applied Science (Expression of Interest)

We are meeting senior leaders for this planned function as we fundraise. This is an exploratory conversation, not an immediately funded salaried vacancy or a guaranteed start date. No cash salary is available today; any equity arrangement must be agreed separately. You are not being asked to begin work by expressing interest.

Use the Apply form below to introduce yourself and indicate your interest. We can discuss mutual fit, scope, funding, compensation, and timing before either side makes a commitment.

Make market simulation useful for consequential customer decisions. Build the applied science function that turns an executive question into a defensible experiment and a decision-ready readout.

Subconscious is building the intelligence to run the economy. We start with a simulator for business decisions, using causal experiments on simulated markets to test products, pricing, messaging, and go-to-market choices before capital is committed. The long-term ambition is to help businesses discover unmet demand and build around what people actually need.

Own scientific scoping, study design, interpretation standards, and the applied science hiring plan. Partner with the Deployment + Operations co-founder on delivery; your accountability is the quality and usefulness of the science, while that role owns the overall engagement.

This is a hands-on leadership role. Set the functional roadmap and propose staffing and spending priorities with the founding team. Hiring and budget commitments depend on approved resources; an established team or funded budget is not assumed.

Use these as an initial agenda, refined with the founders against the product, customer needs, and resources at your start.

• Audit a representative customer question from decision brief through evidence and identify where assumptions could change the recommendation.

• Create a reusable protocol for population selection, experimental design, uncertainty, and human-baseline validation appropriate to the use case.

• Lead an initial customer study and produce a readout that separates estimated simulated effects from claims about real-world outcomes.

• Define which engagements are repeatable, which require new research, and which we should decline.

Clear acceptance criteria before a study starts; reproducible analyses; fewer avoidable redesigns; customer decisions supported by appropriately qualified evidence. Establish baselines for time to a usable answer, review quality, and repeat engagement.

• You have led applied quantitative research or customer-facing science and remained hands-on with experimental design and analysis.

• You can show a study that changed a business decision, explain its limitations, and defend the inference under scrutiny.

• You can work in Python or R and lead scientists across research and enterprise delivery.

We are looking for evidence of judgment, execution, and leadership. A particular company pedigree or previous executive title is not a substitute for that evidence.

We are actively fundraising. Only equity-based compensation is currently available; no cash salary is available today. The equity grant and its terms have not been set for this role and must be agreed before joining. Any future salary, incentive plan, benefits, hiring budget, or paid start date depends on financing and written agreement. Funding and its timing are not guaranteed.

Before deciding to join, discuss the grant structure, fully diluted ownership basis, vesting, dilution, applicable exercise terms, company funding position, and working arrangement. A company valuation is not the cash value of an equity grant.

New York City or San Francisco · In person. The planned role is full time; timing is to be agreed. Expect direct collaboration with the founders, work across scientific and commercial boundaries, and responsibility for doing the early work yourself. The current role does not include visa sponsorship. Confirm practical collaboration hours and any travel expectations during the initial conversation.

Describe a consequential customer decision you helped resolve. What did you measure, what could you infer, and what remained uncertain? Use an existing, non-confidential example.

Please do not share confidential employer or customer materials. Bring your questions about our product, evidence, team, finances, and the authority attached to the role. Mutual diligence matters for a decision of this size.