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Head of Product

hermeneutic Investments
Companyhermeneutic Investments
CategoryProduct
LocationSingapore
RemoteRemote
EmploymentFull-time
LevelDirector
SalaryNot stated by the employer
Posted13 Jul 2026
Last verified9 Aug 2026
SourceEmployer ATS (workable)
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Description
Company Overview hermeneutic Investments is a best-in-class proprietary trading firm and hedge fund. It deploys research-driven discretionary and systematic strategies as well as makes strategic long-term investments. The partners' decade-long history of success in trading and business building and a firm-wide cultural emphasis on alpha generation, open debate, relentless iteration, and teamwork are key to the firm's continued expansion in a challenging market environment that has hamstrung competitors. A hard-wired emphasis on risk management and opportunistic market participation ensure that hermeneutic Investments will continue its growth trajectory in the coming decades. Job Overview We are a discretionary crypto trading firm where traders and researchers make high-conviction decisions using proprietary data, tools, and workflows. Our edge depends not only on the quality of our people, but also on the internal systems that help them investigate markets, test ideas, understand risk, and act quickly with confidence. This is a founding Head of Product role. Today, product work is handled across trading, research, and engineering. Your mandate is to build the product function from the ground up: discovery, prioritization, requirements, roadmap ownership, product quality, adoption, and eventually the team. At our firm, “product” means internal, proprietary systems built for our own traders, researchers, and engineering teams. We do not build retail products, and we do not serve external customers. Your users are expert practitioners inside the firm. Your job is to understand their workflows deeply, identify the highest-leverage problems, and ensure engineering builds the right systems to solve them. You will start hands-on. You will sit with traders and researchers, understand real decisions and investigations, write the early requirements yourself, and establish the standards for how product work should be done. As the scope grows, you will hire and lead a small product team while staying close enough to the trading and research to retain a first-hand understanding of the problems we are solving. Responsibilities 1. Product Function and Operating Model Build the product discipline for the firm. Define how we discover problems, capture requirements, write specifications, review trade-offs, measure adoption, and close the feedback loop with users. 2. Internal Product Roadmap Own the roadmap for internal trading and research systems in partnership with the CIO, engineering, trading, and research leadership. Balance immediate practitioner pain points with longer-term platform foundations, including data quality, provenance, tooling reliability, and system scalability. 3. Practitioner Discovery Work directly with traders and researchers to understand real workflows: how they investigate opportunities, evaluate data, form hypotheses, monitor positions, assess risk, and make decisions. Your job is to understand the underlying decision process and identify what system capability would create the most leverage. 4. Requirements and Product Quality Translate ambiguous practitioner needs into clear, evidence-backed requirements that engineering can build from. Requirements should capture the business context, user workflow, data assumptions, expected behavior, edge cases, success measures, and relevant trade-offs. The goal is not simply to reduce questions from engineering. The goal is to reduce avoidable ambiguity, surface trade-offs early, and ensure the team is building the right thing for the right reason. 5. Trust, Explainability, and Source Transparency Ensure our internal systems are built for trust. Traders and researchers should be able to understand where a number came from, what assumptions sit behind a calculation, why a signal changed, and when human judgment is required. Data lineage, source transparency, explainable calculations, and human-in-the-loop decision-making should be default