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Intern (Fraud)

InComm Payments Brazil 200 candidaturas Ontem

Salário estimado

R$ 7k - 10k/mês

Pleno CLT

Descrição da vaga

Texto agregado para leitura rápida. Confira sempre a fonte original ao enviar a candidatura.

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 an Intern Analyst within the Fraud Strategies team, you will work on high-impact initiatives that directly shape the company’s fraud mitigation and risk management strategies. In this role, you will leverage large-scale datasets to identify emerging fraud patterns and support the creation of data-driven decision-making across the organization. This position is designed for an individual pursuing a bachelor’s degree in a quantitative field to use analytical techniques to leverage solve real-world fraud and risk challenges at scale.

You will work cross-functionally with product managers, data scientists, data engineers, and technical teams to evaluate new and existing datasets, surface actionable insights, and enhance fraud controls across platforms.

Responsibilities

  • Analyze millions of transaction records to identify fraud trends and risk signals using SQL
  • Identify and evaluate new signals from device, behavioral, and geographic data to strengthen fraud prevention strategies
  • Perform in-depth fraud analytics to come up with rule opportunities that
  • Document analytical findings and communicate insights and recommendations to stakeholders and leadership
  • Conduct exploratory data analysis to identify anomalies, key drivers, and significant features that inform fraud risk management decisions

Qualifications

  • Working towards a bachelor’s degree in data science, Computer Science, Statistics, Engineering, or a related quantitative field.
  • Working knowledge of SQL, including the ability to write basic queries for data validation and investigation.
  • Proficiency in SQL for querying and analyzing large datasets.
  • Proficiency in Microsoft Excel, including formulas, filtering, pivot tables, and basic data analysis.
  • Experience with analytics and reporting tools (e.g., Python and Power BI) is a bonus.
  • Demonstrated interest or experience in fraud, cybercrime, payments, or financial risk modeling.
  • Strong written and verbal communication skills with the ability to present complex analyses clearly.
  • Self-motivated, detail-oriented, and able to prioritize work effectively in a fast-paced, collaborative environment.

InComm Payments provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity or national origin, citizenship, veteran’s status, age, disability status, genetics or any other category protected by federal, state, or local law.

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