Fraud Intern

Overview

When you think of InComm Payments, think of Innovative Payments Technology. We were founded over 30 years ago and continue to be a pioneer in the payment (FinTech) industry. Since our inception, we have grown to be a team of over 3,000 employees in 35 countries around the world. We own over 400 global technical patents and a network that includes over 525,000 points of retail distribution that points to our industry expertise.

 

We are significantly growing our Engineering and IT teams in Brazil and are focused on finding talent for various financial technology (Fintech) engineering, database, development, and testing teams.  

 

InComm Payments is highly focused on our people and their growth, and we work hard to make a career at InComm Payments meaningful and rewarding. We value innovation, quality, passion, integrity and responsibility in all that we do, and we are looking for great people to join our team as we move forward towards a very bright future. We anticipate developing future leaders for our teams in Brazil!

 

Benefits include health and dental insurance, meal and restaurant vouchers, fixed monthly stipend for internet and mobile expenses, InComm hardware/software, and annual bonuses! All positions are CLT.

 

You can learn more about InComm Payments by visiting our Website or connecting with us on LinkedIn, YouTube, Twitter, Facebook, or Instagram.

 

About This Opportunity

As a Data Science Intern within the Fraud Decision Sciences team, you will work on meaningful projects that help shape the company’s fraud mitigation strategies. In this role, you will leverage large-scale datasets to identify fraud patterns, contribute to predictive modeling efforts, and support the development of analytical tools that enable data-driven decision-making. This is a high-impact internship designed for students who want to apply advanced analytical techniques in real-world risk and fraud scenarios.

You’ll work cross-functionally with product managers, fraud analysts, and technical teams to explore novel datasets, surface hidden insights, and improve fraud controls at scale.

Responsibilities

• Perform data extraction, cleaning, and validation from large transactional datasets; document assumptions and data quality checks.
• Build exploratory analyses and dashboards to monitor fraud trends, alerts, and loss metrics; identify anomalies and drivers.
• Support development of fraud strategies (rules, thresholds, segments) and help evaluate impact through A/B tests or back-testing.
• Assist in model development lifecycle by preparing features, running experiments, and compiling model performance summaries.
• Create clear, executive-ready summaries of findings and recommendations with guidance from senior team members.
• Collaborate with technology/engineering partners to ensure analytics outputs are reproducible and aligned to production needs.

Qualifications

• 0–2 years of experience in analytics, decision science, risk, fraud, or a related domain (internships/co-ops considered).
• B.Tech compulsory; MBA/MS preferred.
• Working knowledge of SQL and at least one programming language (Python/R) for analysis.
• Understanding of basic statistics and experimentation concepts; ability to interpret model/strategy performance metrics.
• Strong problem-solving skills, attention to detail, and communication skills (written and verbal).

• 0–2 years of experience in analytics, decision science, risk, fraud, or a related domain (internships/co-ops considered).
• B.Tech compulsory; MBA/MS preferred.
• Working knowledge of SQL and at least one programming language (Python/R) for analysis.
• Understanding of basic statistics and experimentation concepts; ability to interpret model/strategy performance metrics.
• Strong problem-solving skills, attention to detail, and communication skills (written and verbal).

• Perform data extraction, cleaning, and validation from large transactional datasets; document assumptions and data quality checks.
• Build exploratory analyses and dashboards to monitor fraud trends, alerts, and loss metrics; identify anomalies and drivers.
• Support development of fraud strategies (rules, thresholds, segments) and help evaluate impact through A/B tests or back-testing.
• Assist in model development lifecycle by preparing features, running experiments, and compiling model performance summaries.
• Create clear, executive-ready summaries of findings and recommendations with guidance from senior team members.
• Collaborate with technology/engineering partners to ensure analytics outputs are reproducible and aligned to production needs.