Data Scientist (NYC, L5/L6)

About the role

Nelo seeks a data scientist to guide credit and marketplace product performance through modeling and experimentation. The role focuses on building analytical solutions for underwriting, personalization, and portfolio management. You will work in an in-office position in New York City.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

The team designs credit, personalization, ranking, and pricing algorithms to improve product performance across user segments.

Continuous experiments generate insights that guide product and algorithm decisions through rigorous analysis.

Machine learning infrastructure initiatives, including machine learning operations and observability, are led to strengthen platform reliability.

Requirements

Candidates accumulate a minimum of 5 years of data science experience in relevant applications and credit products.

Deep understanding of classification models, forecasting models, and core ML techniques is required for effective model development.

Deep knowledge of causal inference, experiment design, and measurement methods supports valid insights from data.

Solving complex optimization problems requires familiarity with formulating and solving constrained optimization in financial contexts.

Advanced use of SQL and Python enables data manipulation and analysis for modeling and decision support.

Practical notes

This position is based in New York City as an in-office role. The company offers 401(k), open PTO, medical coverage, dental coverage, vision coverage, STD coverage, LTD coverage, fertility benefits, relocation support, and a sabbatical program. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data scientists commonly work with SQL and Python in analytics and modeling tasks. Experimentation cultures rely on continuous testing to guide product decisions. Machine learning operations practices support model deployment and monitoring. Credit products use underwriting models to assess risk and portfolio management to optimize performance. Teams often collaborate across product, engineering, and analytics to align on objectives.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Nelo is a leading consumer fintech and e-commerce platform in Mexico, with >$500MM in annualized GMV and >$70MM in annualized revenue. Our mission is to increase consumers' buying power in Latin America, and we do so by building a modern alternative to credit cards.