About TradeVerge AI
Who you'd be joining.
TradeVerge AI builds verified, rules-based automation for the gold market. Our flagship product, Gold Core, is a MetaTrader 5 Expert Advisor with a public, third-party-verified track record on Myfxbook and thousands of members inside our live desk. We are a small, remote-first team that prefers evidence to opinion: every claim we publish is auditable, every release is versioned, and every support answer is written by someone who understands the code.
Data underpins both the research pipeline and the member platform. You will own ingestion, quality checks and the models the rest of the company queries — and you will be the person who says when a number should not be published.
Why this role matters
Opinions are cheap in growth. This role makes sure the loudest argument doesn't win — the evidence does.
What you'll actually do
The day-to-day, not the job-spec version.
- Own funnel, cohort and retention reporting end to end
- Build self-serve dashboards that reduce ad-hoc requests
- Design and read experiments with proper statistical care
- Investigate anomalies before anyone asks
The team and how you'll work
You'll work inside the data function, reporting to the lead for data, and partner day to day with Research, Engineering, Marketing, Finance.
We are async-first across European and American time zones. Expect a short daily written check-in, one team sync a week, and long stretches of uninterrupted focus in between.
Decisions are written before they are made. If a change reaches members, there is a document explaining why, what we expect, and how we'll know if we were wrong.
Tools you'll live in: Python · dbt · Postgres / TimescaleDB · Airflow or equivalent · Metabase.
Your first 90 days
What a good start looks like — we'll agree this with you in week one.
- Complete onboarding with the data function: environments, access, and a walkthrough of how Gold Core actually behaves on a live account.
- Read the last two release memos end to end so you understand the standard of evidence we hold ourselves to.
- Ship one small, real improvement in your area — scoped with your manager in week one.
- Meet everyone you'll work with regularly across Research, Engineering, Marketing.
- Take full ownership of a defined area and become the person the rest of the team asks about it.
- Deliver your first substantial piece of work end to end, including the written rationale behind it.
- Identify the two weakest things in your area and put a fix for one of them on the roadmap.
- Run your area independently, with your manager reviewing outcomes rather than steps.
- Improve one process or piece of tooling so the next person does the job faster than you did.
- Agree the next two quarters of objectives with clear, measurable success criteria.
How success is measured
No vague reviews — these are the things we'll actually talk about.
- Pipeline freshness and completeness SLAs are met without manual babysitting
- Published metrics reconcile with source systems to the cent
- Self-serve questions get answered without an analyst in the loop
Who we're looking for
The full qualification list for this role. Very few people tick every line — apply if the essentials fit.
The core of the role — we read these closely.
- 3+ years of professional experience in data or a directly adjacent discipline
- Demonstrable ownership of work at the scope described above — ideally on a product used by paying customers
- Advanced SQL and a BI tool
- Experiment design literacy
- Ability to present findings crisply
- Advanced SQL and at least one of Python or Scala for pipeline and analysis work
- Building and maintaining ELT pipelines with tested, documented, version-controlled transformations
- Dimensional modelling and metric definition that survives contact with more than one stakeholder
- Dashboarding and self-serve analytics that people actually use
- Data quality practice: freshness checks, contracts, anomaly alerting, lineage
- Working with financial or event-level time-series data at scale
- Understanding of privacy obligations when handling member and payment data
We hire on evidence; credentials are a signal, not a gate.
- Quantitative degree or equivalent applied experience
- Writes clearly and thinks in public — decisions are documented before they are made
- Comfortable being wrong quickly: you look for the evidence that would disprove your own view
- Self-directed across time zones, with reliable follow-through and no need for chasing
- Holds a high bar on member-facing quality, including when nobody would notice the shortcut
- Treats compliance, honesty in marketing claims and risk disclosure as part of the craft, not overhead
- Able to work remote (global) with a stable overlap for one weekly team sync
- Available for full-time work and a reliable home or co-working setup
- Fluent professional English, written and spoken
- Willing to complete a paid, time-boxed craft exercise as part of the process
Nice to have
Genuinely optional — strong candidates rarely tick every box.
- Python
- Subscription analytics
- dbt
Compensation and benefits
$85k – $115k — the band is set by level, published up front and paid the same regardless of how hard you negotiate. Reviewed annually, plus an out-of-cycle review if your scope changes.
- Fully remote, async-first — we optimise for deep work, not meeting attendance
- Competitive base plus performance bonus tied to shipped outcomes
- Private medical cover (US/UK) and a health stipend elsewhere
- £/$2,000 annual learning budget for research, data and conferences
- Top-spec hardware, multi-monitor setup and VPS/data subscriptions covered
- 28 days paid leave plus local public holidays
How hiring works
Five stages, roughly ten days end to end.
- 01Intro call (30 min) — your background, what you want to build next
- 02Craft interview (60 min) — a real problem from our backlog, no whiteboard trivia
- 03Paid take-home or working session (4–6 hrs, compensated)
- 04Team conversation with two people you'd work with daily
- 05Offer, usually within 10 days of the first call
We are looking for pipelines that fail loudly and numbers you would defend in an audit. Everything in our process is designed to surface that rather than test recall.
The craft interview uses a real, recent problem from our backlog. You may use any resource you would use on the job, including documentation and AI tools — we care how you think, not what you memorised.
Take-home work is paid at a fair hourly rate and capped so it never eats a weekend.
You will get a decision, with reasons, after every stage. We do not ghost.