Senior Staff Machine Learning Scientist - Shopping USA, Durham

Nubank; Durham (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

An established industry player is seeking a Senior Staff Machine Learning Scientist to innovate within their Marketplace Business Unit. In this role, you will leverage cutting-edge machine learning techniques to enhance the shopping experience for millions of users. This position offers the opportunity to work on impactful projects, utilizing extensive datasets to develop scalable recommendation systems. Join a forward-thinking team that values collaboration and personal growth, and enjoy the flexibility of remote work along with a comprehensive benefits package that prioritizes your well-being and work-life balance.

Benefits

Remote work with quarterly trips

Top Tier Medical Insurance

Top Tier Dental and Vision Insurance

20 days time off

14 company holidays

Life Insurance and AD&D

Extended maternity and paternity leaves

Learning platform of courses

Mental health and wellness assistance program

401K

Qualifications

• 6+ years of experience in software engineering focused on ML and deep learning.
• Expertise in object-oriented programming languages and ML methodologies.

Responsibilities

• Drive ML initiatives in the Marketplace Business Unit to enhance shopping experiences.
• Collaborate with cross-functional teams to deliver impactful ML solutions.

Skills

Machine Learning

Deep Learning

Python

Problem-solving

Online Experimentation

Education

Bachelor's degree in Computer Science

Master's degree in a related field

Tools

PyTorch

TensorFlow

Ray

Job description

Senior Staff Machine Learning Scientist - Shopping

About Us -

Nubank is the world’s largest digital banking platform outside of Asia, serving over 110 million+ customers across Brazil, Mexico, and Colombia.

Our company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services. Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns. Nu’s impact has been recognized in multiple awards, including Time 100 Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks. For more information, visit our institutional page: Nubank Careers.

About the role

The Marketplace Business Unit at Nubank owns the main page and shopping tab experiences within the app, aiming to create an incredibly simple and lovable purchase journey powered by assertive product and service recommendations. This area represents a massive impact and is critical for the rapidly growing organization. To achieve this, the Marketplace ML Team employs a state-of-the-art, ML-driven algorithmic approach, leveraging Nubank's extensive datasets and optimization techniques to build scalable and reliable shopping intelligence ranking and recommendation systems. We seek individuals with strong problem-solving and coding skills, a history of learning and growth, and a deep interest in ML model, feature, and infrastructure.

We are seeking a Senior Staff ML Engineer/Scientist to drive this area in partnership with the GM for this vertical. In addition to this product area, you will also contribute to additional confidential 0-1 projects in related areas.

Basic Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics or related field
• 6+ years of experience in software engineering with an emphasis on ML, Deep Learning, and online experimentation
• Strong problem-solving skills, with expertise applying ML methodologies to the recommendations and personalization domains.
• Experience working with multiple multi-functional teams (product, science, product ops etc).
• Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, Clojure).
• Working knowledge of latest ML technologies and libraries, such as PyTorch, TensorFlow, Ray, etc.
• Proven track record of being a fast learner and go-getter, with willingness to get out of the comfort zone.

Preferred Qualifications

• 10+ years of experience in ML and building ML models
• Experience in designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods.
• Experience with building ranking and recommendation systems in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments.
• Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact.
• Experience owning and delivering a technically challenging, multi-quarter project end to end.
• 3+ years of experience working in cross-functional and/or cross-business projects, partnering with Product, Scientists, and cross-org leads to shape the team's strategies
• Passionate about helping junior members grow by inspiring and mentoring engineers

Benefits

• Remote work, with quarterly trips to sunny Sao Paulo to build relationships with coworkers.
• Top Tier Medical Insurance
• Top Tier Dental and Vision Insurance
• 20 days time off, 14 company holidays, and great culture that emphasizes work-life balance.
• Life Insurance and AD&D
• Extended maternity and paternity leaves
• Nucleo - Our learning platform of courses
• NuCare - Our mental health and wellness assistance program
• 401K
• Saving Plans - Health Saving Account and Flexible Spending Account

Apply for this job

* indicates a required field

First Name *

Last Name *

Preferred First Name

Email *

Phone *

Resume/CV *

LinkedIn Profile *

Do you have 6+ years of post-graduation hands-on ML Expertise? *

Please briefly describe your experience applying Machine Learning to recommendations or personalization problems in the shopping domain. *