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 product principles.
Act as the connective tissue between trading, research, engineering, and management. You will surface trade-offs clearly, challenge assumptions constructively, and help the firm make better prioritization decisions.
Start as the sole product leader, then build the team as the work scales. Hire and develop product managers, set the bar for discovery and specification quality, and create a product culture suited to a high-performance trading environment.
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Spend significant time with traders and researchers to understand real decisions, workflows, investigations, datasets, and pain points.
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Distinguish stated feature requests from underlying practitioner needs.
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Translate trading and research workflows into product opportunities, clear requirements, and buildable product decisions.
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Write clear product requirements for internal systems, workflows, data products, analytics, and decision-support tools.
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Partner closely with engineering to shape feasible, scalable solutions and surface technical trade-offs early.
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Prioritize product work based on expected trading, research, operational, or risk impact.
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Define success measures for product work, including faster research cycles, better data quality, reduced manual work, improved decision confidence, risk reduction, stronger adoption, or, where measurable, PnL contribution.
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Defend foundational work — such as data quality, lineage, reliability, and platform architecture — when it is more important than a visible front-end feature.
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Evaluate external data providers and tooling vendors where relevant, including API quality, coverage, latency, reliability, cost, and integration complexity.
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Establish product rituals, documentation standards, and feedback loops.
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Advise the CIO on product priorities, practitioner needs, product-engineering trade-offs, and sequencing.
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Hire and lead a small product team once the scope requires it.