Riverside, CA

Salary
Posted
Jul 6, 2026
Location
Riverside, CA
Last confirmed open
Jul 21, 2026

What this job asks for AI summary

A leadership role heading a data science team at a Latin American B2B fintech, with a focus on credit risk and fraud. Day-to-day work involves building and overseeing machine learning models, designing scalable data pipelines, and running predictive analytics to support business decisions. The role also carries responsibility for team mentorship, cross-functional collaboration, and shaping the broader analytical strategy.

Senior level · National

Must have (4)
machine learningpredictive analyticsfraud detectiondata modeling

We read this from the posting text with AI. Skim the description below before ruling yourself out.

How this req sits in the market our data

What gives you an edge
data modeling10%machine learning12%

Rare in this occupation — lead with these, and say what you built with them.

What the occupation pays Median $178,991 (middle half $141,096–$225,584).

Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.

Why we read it this way (7)

This role is based in Latin America (LATAM) and is explicitly open to all LATAM candidates — it is not a US-based position. The US-based flag is set to false accordingly.

The posting is primarily a people-management role (leading and managing a data science team, mentoring, strategic planning), which maps best to Computer and Information Systems Managers (11-3021). However, the JD also emphasizes hands-on technical depth in ML and predictive analytics, making Data Scientists (15-2051) a plausible runner-up.

No specific technologies, languages, or tools (e.g. Python, SQL, Spark) are named anywhere in the posting — only domain capabilities and responsibilities are described. The skills extracted reflect the concrete domain competencies that are explicitly required.

No compensation figures, experience minimums, or degree requirements are stated in the posting.

The hybrid work model is described as flexible with no enforced minimum office days for most roles, but is not fully remote — candidates are expected to spend time in-office organically.

Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): credit risk modeling.

This role's work location reads as outside the US — the candidate pool, comp, and contention benchmarks here are US-only (BLS employment, Adzuna/USAJOBS demand, and certified H-1B wages), so treat them as a rough US reference, not a local market.

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