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Data Scientist, London

London
Posted about 20 hours ago
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Data Scientist, London

Company

Transak

Location

London, United Kingdom

Type

Remote, Onsite

About The Company

Our mission is that “Any financial application can onboard any user, anywhere in the world, in 1 click.” Transak provides onboarding to financial applications through authentication, KYC, risk checks, and fiat on/off ramps. This is a next generation of infrastructure for the next generation of financial applications that are built on blockchain and stablecoin rails. Our API and widget-based solutions are used by top partners like MetaMask, Coinbase, Ledger, and Trust Wallet to enable seamless onboarding of over 10 million users across over 450 active applications. We have raised over $37M from top-tier investors including Consensys, Tether, and Animoca Brands.

About The Role

The mandate is easy to state and hard to deliver: stop fraud while approving as many legitimate transactions as possible. How you do it is yours to decide — deterministic heuristics, machine learning, AI agents, or whatever the problem demands — and the adversaries on the other side are among the most sophisticated in the world.

You'll take genuine end-to-end ownership of how Transak detects and prevents fraud, as part of a small, senior team. The commercial stakes are direct: every basis point of fraud, and every unit of unnecessary friction, maps straight to revenue and to real users who can or cannot transact.

Machine Learning & Artificial Intelligence

The problem at scale

This is a genuinely hard applied problem, and the conditions to do something exceptional with it are already in place.

  • Tens of millions of transactions of history to learn from, with 60+ fields each.
  • ~1,000 live risk signals available per decision, across 13 providers.
  • ~10,000 orders per hour at peak, with value well into seven figures in a single peak hour.
  • Tens of thousands of attempts in a single coordinated attack, over a matter of days.
  • The adversary is intelligent and adaptive. This is not a static classification task — the distribution shifts the moment you respond, because the counterparty is actively working to defeat you.
  • The modelling layer is largely greenfield. The data and the scale already exist; modern machine learning has not yet been applied to them properly. The opportunity to do so — and to own it — is wide open.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

Start with a chat, not a search bar

Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.

P

Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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It searches the market for you

Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.

Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

See breakdown
Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

See breakdown
Strong

Only hits

No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.

Key responsibilities

  • Own fraud, chargeback and transaction-risk models end to end — from framing the question, to features and rules, to what ships, to the thresholds that set policy.
  • Build and tune the machine-learning and signal layer that operates alongside our external risk vendors.
  • Set the direction of where risk modelling goes next, and bring the team with you.
  • Act as a data scientist for the wider business — making data accessible through the Snowflake warehouse, self-serve tooling, dashboards and internal data assistants that let any team query our data directly.
  • Take on high-leverage product, growth and experimentation problems as they arise.
  • Help advance how we apply AI internally, from agentic coding assistants to internal copilots and novel uses of large language models.

What we're looking for

We weight how you think far more heavily than any checklist of tools.

  • Strong mathematical and statistical foundations, and a genuine pull toward hard, quantitative problems.
  • A self-starter who identifies the important problem, scopes it, and acts without waiting to be directed.
  • A capable engineer and analyst — fluent in Python and SQL, and able to take a model from idea to something that runs and ships.
  • Intellectually honest about uncertainty, and rigorous in evaluating your own work.
  • A genuine interest in crypto, payments, fraud and the data they generate.

Engineering & Technology

You might be an exceptional recent graduate, a PhD, or a year or two into your career and ready for far more ownership than your current role allows. The non-negotiables are raw ability and drive.

Desirable

None of the following are required, but any would strengthen an application.

  • A track record of exceptional output — a strong degree from a leading university, research, competition results (Kaggle, olympiads), open-source contributions, or something you built that people use.
  • Exposure to fraud, risk, payments, crypto, or other adversarial, high-stakes machine learning.
  • Experience deploying and maintaining models in production, including monitoring and drift.
  • Depth in experimentation or causal inference.
  • A habit of building your own tools and automations.

Working with us

  • Autonomy, against a clear results bar. You'll have the freedom to try new models, tools and approaches; we judge outcomes, not process or hours.
  • AI-native by default. We use Claude, Codex and whatever is current, heavily, across engineering and analysis — and expect the same of you. Leverage is the point.
  • A small, technical, high-trust team. Short feedback loops, little bureaucracy, and colleagues who hold a high bar.

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Life at Transak

Transak is a developer integration for a fiat-to-crypto payment gateway. This solves the important problem of allowing mainstream people and businesses to access crypto and blockchain. It does this by integrating local compliance, payment methods, and liquidity from around the world. Transak is backed by Consensys, The LAO, Lunex, and Mycelium Ventures.

Thrive Here & What We Value

  • Collaborative and supportive work environment
  • Emphasis on innovation and creativity
  • Focus on delivering high-quality results
  • Encouragement of professional development and growth opportunities
  • Commitment to sustainability and social responsibility
  • Dynamic, inclusive, and supportive work culture
  • Opportunities for professional growth and career advancement
  • Chance to be at the cutting edge of the blockchain industry
  • Equal opportunity employer with comprehensive benefits package

Additional Benefits

  • Innovative team that values collaboration and teamwork
  • Focus on expanding Transak's enterprise solutions in the web3 domain
  • Opportunity to make a significant impact in the web3 sector
  • Competitive Compensation Package And Comprehensive Benefits Offering
  • Supportive Leadership with clear growth roadmap
  • Commitment to work-life balance and personal health

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Skills

Python
SQL
Machine Learning
Artificial Intelligence
Statistical Modeling
Data Analysis
Fraud Detection
Snowflake
Causal Inference
Experimentation
Large Language Models
Risk Modeling

Location

London, England, United Kingdom

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