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Requirements:
3+ years of hands-on experience in data analytics.
Payments fraud experience.
Experience working with external stakeholders/merchants.
Expertise in SQL with the ability to write and optimize complex quries.
Strong analytical skills, problem-solving mindset, and ability to extract insights from large datasets.
Ability to work cross-functionally in a fast-paced, data-driven environment.
Fluent English with excellent written and verbal communication skills.
Experience with Python (Pandas, etc.) for data analysis - a plus.

Requirements:
3+ years of hands-on experience in data analytics.
Payments fraud experience.
Expertise in SQL with the ability to write and optimize complex queries.
Strong analytical skills, problem-solving mindset, and ability to extract insights from large datasets.
Ability to work cross-functionally in a fast-paced, data-driven environment.
Fluent English with excellent written and verbal communication skills.
Experience with Python (Pandas, etc.) for data analysis - a plus.

Requirements:
Experience: Minimum of 3+ years of applied Data Science experience with a proven track record across fintech domains, with experience in fraud, risk, or payments preferred.
Production Expertise: Proven, hands-on experience deploying and maintaining machine learning models in high-traffic production environments is required, with real-time experience preferred.
Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.
Data Environment: Robust experience working within cloud data environments like Databricks, and querying/manipulating large-scale datasets in data warehouses like BigQuery.
Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow.
Business-Impact Focus: A strong ability to translate raw model results into real-world business outcomes. You know how to balance technical model performance (precision/recall) with financial impact, operational realities, and the user experience.

Requirements:
Experience: 1-2 years of hands-on professional experience as a Data Scientist or Data Analyst in a data-intensive environment.
Data Science Tech Stack: Solid proficiency in Python (Pandas, NumPy, Scikit-Learn) and strong capability writing and optimizing SQL queries.
Modern Data Infrastructure: Exposure to or basic hands-on experience working within environments like Databricks and data warehouses like BigQuery.
Academic Background: Degree in a quantitative field (Computer Science, Statistics, Data Science, Industrial Engineering, or equivalent).
Business-Impact Focus: An understanding of how to look past raw model metrics (precision/recall) to appreciate the operational impact of data decisions.
Communication: Fluent English with the ability to communicate technical findings clearly to team members.
Bonus Points
Prior exposure to or hands-on projects involving machine learning models in a live, real-time production environment.
Familiarity with MLOps or orchestration tools such as MLflow or Airflow.
Previous domain exposure in FinTech, e-commerce, payments, or trust & safety.

Requirements:
What we're looking for:

We're not looking for a process operator. We're looking for a commercial product leader who happens to love fintech - operating at the exceeding-the-standard bar from day one:

Ownership & agency. You own outcomes end to end - including the decision to continue, scale, reshape, or stop. You set the decision framework (problem, success, assumptions, tradeoffs, kill-signals) before work starts and keep the team honest against it. You don't wait for someone to tell you how the work will be judged.

Operates in ambiguity. When the problem isn't defined, you make assumptions explicit, pressure-test them fast, and move. A fast, imperfect call beats waiting for perfect analysis - and you know the blast radius.

Judgment & taste. You bring a clear point of view, synthesize inputs into a defensible recommendation, and don't default to the average of what stakeholders said. You can name weak logic and a solution that isn't good enough yet - including in others' work.

Commercial instincts. You think in pricing, margin, take-rate, unit economics, and vendor cost. You can read a P&L and a contract and tell us what's hiding in it.

Show, don't tell. You'd rather put a model, prototype, or one-pager in front of the team than run another meeting.

AI-driven velocity. You build tools, prompts, and workflows that embed your thinking so the team moves without waiting on you. The bottleneck isn't you - that's the point.

Customer obsessed. You're in our members' businesses regularly. You can tell a real pattern from a loud opinion.

High ownership, low ego. You see a fire, you put it out, even if it's not "yours."

Experience:

8+ years of product management experience.

Deep domain in payments, fintech, or B2B financial products - you've shipped against Stripe / Adyen / Square / Plaid / a BaaS platform / lending infra.

Track record of building 0-to-1 and scaling live products to find PMF.

Comfortable as the named executive counterpart to vendor and partner leadership.

US - international expansion (UK/EU/AU) in payments.

Experience with regulatory and fraud challenges.