![LexisNexis Risk Solutions](https://media.trabajo.org/img/noimg.jpg)
Data Scientist II
3 semanas atrás
Are you interested in pursuing a career working with credit applications, specifically in risk and fraud
About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below,
About our Team
Our Risk Business Analytics family contains over 100 data scientists in fast-paced environment. We are innovators, passionate about challenging the status quo, and improving outcomes.
About the Role
This position exists to conduct both data handling and analytics/statistical modeling for credit application (risk and fraud).
Responsibilities
- Collecting, aggregating, matching, consolidating, and confirming data across business units, ensuring quality and accuracy.
- Identifying, researching, programming (Python and R), and analyzing data for accurate processing, focusing on designing solutions for assigned business areas.
- Recommending process and system improvements aligned with business goals, enhancing overall efficiency.
- Creating models and analytics for risk, fraud, and collections across sectors, both internally and for clients."
- Developing scoring models, conducting statistical analysis, and providing code for production scoring validation. Clearly summarizing, documenting, and communicating analytic work/results to internal and external clients, and stakeholders.
- Have Bachelor's degree in statistics, computer science, mathematics, actuarial science, engineering or other quantitative science.
- Experience in data manipulation, cleansing, relational databases, and statistical modeling.
- Have R or Python knowledge
- Have intermediary English fluent in Portuguese. Spanish is nice to have.
- Be an adaptable learner with a collaborative approach and excellent communication skills, adept at explaining technical outcomes to diverse audiences.
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