Intern (Data Science)

Intern (Data Science) - Detalhes da Vaga. ● 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 bachelors 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 SQLIdentify and evaluate new signals from device, behavioral, and geographic data to strengthen fraud prevention strategiesPerform in-depth fraud analytics to come up with rule opportunities thatDocument analytical findings and communicate insights and recommendations to stakeholders and leadershipConduct exploratory data analysis to identify anomalies, key drivers, and significant features that inform fraud risk management decisionsQualifications:Working towards a bachelors degree in data science, Computer Science, Statistics, Engineering, or a related quantitative fieldWorking knowledge of SQL, including the ability to write basic queries for data validation and investigation. ●Proficiency in SQL for querying and analyzing large datasetsProficiency 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 modelingStrong written and verbal communication skills with the ability to present complex analyses clearlySelf-motivated, detail-oriented, and able to prioritize work effectively in a fast-paced, collaborative environment. Requisitos para participar do processo. ● . O que oferecemos.