Forward Deployed Engineer (AI)

Location: São Paulo, hybrid, with in-person time at customer sites

Company

hirify.global develops AI-powered solutions that help enterprises improve high-stakes credit, risk, customer acquisition, and fraud decisions.

What you will do

• Work directly with clients to understand decision workflows, data infrastructure, operations, and business pain points.
• Build and deploy AI-powered solutions for credit, risk, customer acquisition, and fraud use cases.
• Assess customer data quality and prepare the right data for consumption by the Data Platform team.
• Translate business requirements into technical specifications and technical outputs into clear business narratives.
• Collaborate with Deployment Strategists, Data Scientists, Product, and Engineering teams to iterate quickly on real-world solutions.
• Communicate business impact to data specialists, operational teams, and C-level stakeholders.

Requirements

• Background in statistics, econometrics, or data science, through a degree or equivalent practical experience.
• At least 2 years of hands-on experience solving business problems with data in credit, risk, fraud, growth, acquisition, or a related decision-heavy domain.
• Proficiency in Python and SQL, with experience using APIs and basic software engineering practices such as version control, deployment, and testing.
• Familiarity with real-world data structures and messy production data.
• Experience with experimentation and causal inference, including A/B testing, incrementality, or quasi-experimental methods.
• Ability to explain model and data insights to senior business stakeholders, including CFOs, CROs, and Heads of Risk.

Nice to have

• Previous experience at a fintech or high-growth technology company.

Culture & Benefits

• Hybrid work with in-person collaboration at customer sites.
• Participation in an FDE cohort with Data Science pairing and shared learning across use cases.
• Direct exposure to Product and Engineering decision-making through field feedback.
• Focus on shipping practical solutions in weeks rather than leaving data science work in notebooks.