Quantitative Researcher (Client Solutions Lead)

About BTSE:
BTSE Group is a global leader in fintech and blockchain technology, anchored by three core business pillars: Exchange, Payments, and Infrastructure Development. Serving over 100 corporate clients worldwide, we provide white-label exchange and payment solutions. Our offerings encompass everything from exchange infrastructure hosting and development to custody, wallets, payments, blockchain integration, trading, and more. We are looking for talented professionals in marketing, operations, customer support, and other departments. The roles offered may be on-site, remote, or hybrid, in collaboration with our local partner.

About the opportunity:
You are the bridge between the platform and its users. You run the structured discovery process with the first client — mapping how their research teams actually work, what data they use, what their output looks like, and where AI can create the most value. You then translate those findings into the prompts, exemplars, and configurations that shape every AI output the client sees. During the pilot, you sit with researchers during live market hours, collect feedback, and iterate daily. Your work sets the quality ceiling for the entire platform. For the first engagement, you need deep crypto market knowledge. Over time, this role evolves into the repeatable methodology for onboarding any enterprise client in any finance domain.

Responsibilities

Run the Research Process Discovery for the first client: structured questionnaires and interviews across their research departments, synthesised into workflow maps and platform requirements.

Design and write the system prompt library for all AI task types, tailored to how the client’s researchers think, analyse, and communicate.

Create gold-standard few-shot exemplars from real historical market events that teach the AI what good output looks like.

Define event priority classification: which market events require immediate AI analysis, which are batch, which are background.

Define signal thresholds for AI alerting based on the client’s actual analytical frameworks.

Sit with researchers during live market hours from Week 3 onward. Note every output error, every missing context, every wrong format. Update prompts and exemplars daily.

Review the feedback pipeline weekly: validate training data candidates, flag systematic errors, replace weak exemplars with production examples that users validated.

Document the discovery and onboarding methodology so it can be repeated for the next client in a different finance domain.

Requirements

5+ years in crypto research, trading, or crypto data product roles.

Deep understanding of crypto market events and their price impact: regulatory, protocol-level, macro, sentiment.

Hands-on experience with on-chain analytics tools (Glassnode, CryptoQuant, Dune, Nansen, or equivalent).

Comfort with Python for data analysis and prompt engineering.

Excellent written communication — the prompts and exemplars you create directly shape every AI output the client sees.

Ability to evaluate AI output quality from a practitioner’s perspective — you know the difference between impressive-sounding and actually useful.

Nice to have

Prior role at a crypto hedge fund, market maker, or crypto-focused research firm.

Experience with crypto derivatives: perpetual swaps, funding mechanics, options, liquidation mechanics.

Prior experience using LLMs for financial research.

Client-facing consulting or solutions engineering experience.

Network in the crypto research community.