WHO WE ARE IS WHAT WE DO.

Deel is the all-in-one payroll and HR platform for global teams. Our vision is to unlock global opportunity for every person, team, and business. Built for the way the world works today, Deel combines HRIS, payroll, compliance, benefits, performance, and equipment management into one seamless platform. With AI-powered tools and a fully owned payroll infrastructure, Deel supports every worker type in 150+ countries—helping businesses scale smarter, faster, and more compliantly.
Among the largest globally distributed companies in the world, our team of 6,000 spans more than 100 countries, speaks 74 languages, and brings a connected and dynamic culture that drives continuous learning and innovation for our customers.

SUMMARY

The Data Scientist is responsible for analyzing complex data to uncover valuable insights, build predictive models, and support data-driven decision-making across the company. You will work closely with various teams to design and implement models that drive business outcomes, optimize processes, and solve problems across departments such as Product, Marketing, and Operations. This role is integral to Deel’s data-driven culture, making it a core function within the company’s broader analytics and data strategy.

QUALIFICATIONS

• Background and experience in data/backend engineering, ideally in production environments (3+ years) (Mid/Senior Level role)
• High proficiency in Python and its data science stack.
• Background and hands-on experience (2+ years) in applied algorithmic research, specifically in information retrieval, text processing, computer vision (CV), computational linguistics (NLP) and machine learning
• Track record of good written and verbal communication of complex things in a simple way as well as ability to collaborate well with people from different backgrounds and professions
• A master’s degree or higher
• Experience in NLP is a plus

• Solve real world problems using applied algorithmic research and Data Science practices
• Implement functionality which can be served in production for internal customers as well as external customers
• Designing, building and maintaining data sets
• Data cleaning
• Feature engineering and extraction
• Modeling
• Experimentation
• Online and offline demonstrations of capabilities
• Building end-to-end machine learning pipelines
• Conduct reproducible research
• Collaborate with Engineering, Operations, Product Management and other functions in the company to deliver algorithmic solutions
• Apply software engineering practices in our code that implements our research and its infrastructure
• Produce high quality, clean, maintainable reproducible research and code