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Fudo is a fast-growing restaurant management software startup operating across Latin America (market leader in Chile and Argentina, expanding in Mexico, Colombia, and Brazil). The company combines hospitality industry expertise with technology to help thousands of restaurants improve operations through management software and mobile applications.
You will transform data into commercial intelligence to drive lead generation, conversion, and revenue growth. The role bridges data science, AI, and go-to-market strategy, designing and building data solutions, predictive models, and AI-powered tools that identify and prioritize leads, anticipate sales opportunities, and boost sales team productivity. You'll work cross-functionally with development and product teams to move solutions from prototype to production.
Key responsibilities include:
- Generating commercial intelligence: defining Ideal Customer Profile (ICP) by country and segment, producing insights to improve sales training and rep performance
- Designing and building a Lead Generation Machine: identifying, enriching, scoring, and prioritizing restaurant prospects for outreach
- Developing propensity-to-convert (CVR), lifetime value (LTV), and upsell/cross-sell recommendation models
- Applying generative AI and LLMs to sales operations: data enrichment, sales briefs, and prospecting/follow-up automation
- Prototyping, validating, and productionizing solutions with development and product teams
- Measuring impact on sales rep productivity and MRR per rep; communicating results to commercial leadership
The role is semi-remote (hybrid) based in Buenos Aires, in a collaborative, fast-moving environment where priorities evolve and ambiguity is common.
REQUIREMENTS:
- Bachelor's degree in Engineering, Computer Science, Mathematics, Statistics, Economics, or related field
- 5+ years of applied data science experience in business contexts, ideally in GTM, Growth, or Marketing at SaaS or technology companies
- Advanced proficiency in Python and SQL; experience building predictive models (propensity, scoring, LTV, churn, recommendation)
- Hands-on experience with LLMs and generative AI in production use cases
- Ability to take ambiguous problems and convert them into concrete solutions with a product mindset
- Strong communication skills: translating technical results for non-technical stakeholders
- Flexibility and comfort operating in a new function where priorities are defined iteratively
