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Trainee - Data Science - VN (US Shift-Working)

RackspacePosted 3/27/2025

Rackspace logo

Trainee - Data Science - VN (US Shift-Working)

Rackspace

Job Location

Job Summary

Join Rackspace Technology as a Trainee - Data Science in Vietnam. As a member of the Data Science team, you will work on cutting-edge customer projects, develop analytical models, and implement machine learning techniques. You will be responsible for analyzing trends, making better decisions, and contributing to data science discussions. With flexible remote work options and a commitment to equal employment opportunity, this role is ideal for fresh graduates with enthusiasm, creativity, and business acumen. You will have the opportunity to learn new data science techniques, programming languages, and explore their viability on current projects. Our team values curiosity, ownership, and a drive to improve, and we offer flexible remote work options, $4,000/year travel stipends, and equity in a fast-growing company.

Job Description

As a member of the Data Science team, you will focus on working on cutting-edge customer projects in diverse areas from product recommendations, fraud detection, supply chain optimization, and demand forecasting.
Full of energy, fresh ideas, and ambition, you are the sort of person who loves learning new things, is open to being challenged and has the confidence to challenge us too.
We are looking for fresh graduates (with < 1 yr of post-graduation experience) with the enthusiasm, creativity, and business acumen to drive real innovation and become our future leaders.
In this role, you should be highly analytical with a knack for analysis, math, and statistics. Critical thinking and problem-solving skills are essential for interpreting data. We also want to see a passion for machine learning and research.
Your goal will be to help our customers analyze trends to make better decisions.
- To develop analytical models for insight generation that improve business decision-making.
- Responsible for experimenting with and implementing machine learning techniques on internal/external customer projects.
- Responsible for learning new data science techniques/programming languages and exploring their viability on current projects.
- Will contribute to data science team discussions, providing insight into current approaches and methods,
- Follows established MLOps practices in software development.
- Data science model selection and implementation.
- Working independently while collaborating well within the team.
- Have worked autonomously and delivered results on schedule
About Rackspace Technology
We are the multicloud solutions experts. We combine our expertise with the world’s leading technologies — across applications, data and security — to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future. Named a best place to work, year after year according to Fortune, Forbes and Glassdoor, we attract and develop world-class talent. Join us on our mission to embrace technology, empower customers and deliver the future.
 
 
More on Rackspace Technology
Though we’re all different, Rackers thrive through our connection to a central goal: to be a valued member of a winning team on an inspiring mission. We bring our whole selves to work every day. And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe. We welcome you to apply today and want you to know that we are committed to offering equal employment opportunity without regard to age, color, disability, gender reassignment or identity or expression, genetic information, marital or civil partner status, pregnancy or maternity status, military or veteran status, nationality, ethnic or national origin, race, religion or belief, sexual orientation, or any legally protected characteristic. If you have a disability or special need that requires accommodation, please let us know.