Data Scientist II, Applied ML

United States Digital Space LLC; São Paulo; Presencial; BRL 120 000 - 240 000; Tempo integral

Resumo da oferta

United States Digital Space LLC is seeking a Data Scientist with strong ML and production experience to join our Data & AI team in Brazil. You will drive end-to-end model development, productionize models, and collaborate with Ops, Product, Fraud, Compliance, and Credit teams to deliver real business impact.

Responsibilities include building scalable ML solutions, applying statistical methods, and ensuring reliable APIs and data pipelines.

Qualificações

• 3+ years of experience in Data Science/ML roles or 2+ years with a PhD in a quantitative field
• Own end-to-end model development including productionization
• Strong Python programming and SQL querying skills
• Experience with ML frameworks and API development for production systems
• Ability to apply statistical techniques (hypothesis testing, A/B testing) and think statistically
• Strong software engineering fundamentals and collaboration with cross-functional teams

Responsabilidades

• Drive Data & AI solutions from inception to deployment to manage risk and improve customer experience
• Own the full machine learning lifecycle from problem to productionization and monitoring
• Collaborate with cross-functional teams (Ops, Engineering, Product, Fraud, Compliance, and Credit)

Conhecimentos

Python

SQL

ML frameworks

APIs

Statistical methods

Productionization

Communication

Descrição da oferta de emprego

Why join us

the company is the intelligent finance platform that enables companies to spend smarter and move faster in more than 200 markets. By combining global corporate cards and banking with intuitive spend management, bill pay, and travel software, the company enables founders and finance teams to accelerate operations, gain real-time visibility, and control spend effortlessly. the company’s AI-native automation and world-class service eliminate manual expense and accounting tasks for customers so they can focus on what matters most. Tens of thousands of the world's best companies run on the company, including DoorDash, Coinbase, Robinhood, Zoom, Plaid, Reddit, and SeatGeek.

Working at the company allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career.

Data at the company

The Data organization develops infrastructure, statistical models, and products using financial data. Our Scientists and Engineers work together to make data —and insights derived from data — a core asset across the company. Our work is ingrained in the company’s decision-making process, in the efficiency of our operations, in our risk management policies, and in the second-to-none experience we provide our consumers.

What You’ll Do

Our Data Scientists are responsible for the entire model development lifecycle, from conception with stakeholders, through model development and productionization, to following through to see that the desired business impact is achieved — including circling back with stakeholders to make product or strategic decisions.

Responsibilities

• Drive Data & AI solutions from inception to deployment to efficiently manage risk and/or improve customer experience.

• Be responsible for the full machine learning lifecycle — problem identification, model design, training, productionization, and monitoring.

• Partner with cross-functional teams (Ops, Engineering, Product, Fraud, Compliance, and Credit).

Requirements

• 3+ years of experience in Data Science/ML roles, or 2+ years with a PhD in a quantitative field

• Demonstrated ability to own end-to-end model development, including productionization

• Expertise in Python programming, SQL queries, and ML-related frameworks

• Ability to apply statistical techniques such as hypothesis testing and A/B testing, and to approach problems with a statistical mindset

• Strong software engineering fundamentals, including experience with API development and integrating ML systems into production services

• Strong communication skills and the ability to collaborate with various stakeholders, both technical and non-technical

Nice to Have

• Experience working with real-time models

• Advanced degree (MSc/PhD) or published research in Machine Learning or a related field

• Previous experience in the risk domain (fraud, AML, and/or credit) or building customer-facing ML models (suggestions/automations)

• Experience in the fintech industry