Applied Scientist II (Credit modeling)

Rocket Lab; São Paulo; Híbrido; BRL 80.000 - 120.000; Tempo integral

Resumo da oferta

Rocket Lab is seeking an Applied Scientist to join our Payments team in São Paulo, Brazil. In this role, you will leverage your expertise in machine learning and quantitative analysis to improve Uber's payment process. The ideal candidate will possess strong communication skills and industry experience in ML frameworks, contributing to cross-functional projects and influencing business decisions.

Qualificações

• Experience in credit modelling.
• Senior or Staff level experience as a Data Scientist or Machine Learning Engineer/Scientist.
• Experience developing complex software systems.

Responsabilidades

• Design and deploy machine learning and statistical models.
• Collaborate with teams to drive system development from conceptualization to production.
• Present findings to senior management to influence decisions.

Conhecimentos

Machine Learning

Data Analysis

Strong Communication Skills

Ferramentas

Python

Tensorflow

Pytorch

Spark SQL

Descrição da oferta de emprego

About the team

We are looking for an Applied Scientist (Machine Learning Scientist) to join our Payments team and make meaningful contributions to our mission to streamline and optimize Uber’s global payment experiences.

In this role, you will be able to use your strong quantitative skills in the fields of machine learning, statistics, economics, operations research, as well as underwriting principles and practices, to improve the Uber Payments experience.

We are looking for candidates with a passion for solving new and difficult problems with data and we specifically seek candidates with experience in underwriting and developing models to evaluate financial exposure, as these skills are crucial for effectively managing and optimizing payment processes.

About the Role

• Design, build, deploy machine learning, statistical, optimization models into Uber production systems for a wide range of applications.
• Design, build, and deploy data pipelines into Uber production.
• Collaborate with multi-functional teams across areas such as product, engineering, operations, and design to drive system development end-to-end from conceptualisation to productionization.
• Understanding product performance and to find opportunities within data.
• Present findings to senior management to influence business decisions.

Technical Skills

Required:

• High-intermediate to fluent EN skills.
• Senior and/or Staff seniority as a Data Scientist, Applied Scientist and/or Machine Learning Engineer/Scientist.
• Experience working with credit modelling
• Industry experience in ML frameworks (e.g. Tensorflow, Pytorch, or XGBoost) and complex data pipelines; programming languages such as Python, PySpark, Spark SQL.
• Thought leadership to drive multi-functional projects from conceptualisation to productionization.
• Strong communication skills and can work effectively with cross-functional partners.
• Experience developing complex software systems, scaling them with production quality deployment, monitoring, and reliability.

Please note: this hybrid position is based in São Paulo, Brazil - welcoming both local professionals and those open to relocating to São Paulo.

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together.

Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to [e-mail].