Strategy & Corporate Development Analyst

About the role

The role operates from Mexico City under full-time employment terms.

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

Spreadsheets and modeling tools project financial outcomes to evaluate scenarios, plan investments, and test value opportunities.

M&A and investment opportunities are evaluated through deal models, target financial analysis, and coordinated cross-functional due diligence.

Presentations, Board updates, investor materials, and executive decision-making inputs are designed to communicate strategic options effectively.

Requirements

A Bachelor's degree in Finance, Economics, Business, Engineering, or a related field is required.

1-3 years of experience in management consulting, investment banking, corporate strategy, or a related analytical role is required.

Expert-level Excel proficiency is required to construct sophisticated financial models for M&A valuation, financial forecasting, and data analytics.

High analytical capability and problem-solving skills are required to distill complex analyses into clear, actionable insights; comfort using AI tools such as Claude to support analysis is specified.

Familiarity with lending businesses, financial institutions, payment and collection curves, cohort analysis, and core unit-economics and credit KPIs (LTV/NPV, loss rates, portfolio yield, etc.) is required.

An ownership mentality is required, including high organization, self-motivation, and the ability to manage multiple priorities in a fast-paced environment.

Effective communication and collaboration skills are required, including experience working in cross-functional teams and the ability to craft data-backed narratives for senior audiences.

Fluency in both written and spoken English and Spanish is required; fluency in Portuguese is a plus.

Practical notes

This role is based in Mexico City under full-time employment terms and is an equal opportunity position.

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

Financial services analysts use spreadsheets and modeling tools to project outcomes and test scenarios.

Work often involves interpreting data, communicating findings, and aligning with stakeholders.

Machine learning and data science methods support decision-making in credit and fintech environments.

Cross-functional collaboration is common when aligning strategy across product, operations, and finance.

Presentation design helps translate complex analysis into clear recommendations for leadership.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

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.