Trainee Data Science

Summary

Join Rackspace Technology as a Data Scientist and develop analytical models to enhance business decision-making. You will experiment with and implement machine learning techniques on customer projects, learn new data science techniques and programming languages, and contribute to team discussions. Responsibilities include model selection and implementation, following MLOps practices, and working both independently and collaboratively. The ideal candidate possesses a degree in a relevant field, proficiency in machine learning and statistics, and experience with various programming languages and tools. A strong analytical mindset, excellent communication skills, and experience with cloud computing are also essential.

Requirements

• Degree in Management Information Systems (MIS), Data Science/Analytics, Computer Science, Statistics and/or equivalent formal training
• Proficient with machine learning and statistics
• Knowledge in classification (random forest, logistic regression), regression (linear, XGBoost) and clustering (k-means)
• Software development experience
• Proficient in Python/R/SAS, SQL, Data Visualization (e.g. Tableau or Power BI), deep learning frameworks, Spark
• Excellent communication skills
• Proficient with cloud computing in environments such are AWS, Google Cloud, Microsoft Azure

Responsibilities

• Develop analytical models for insight generation that improves 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
• Contribute to data science team discussions, providing insight on 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

Preferred Qualifications

• Participated in numerous data science projects
• Utilized several data science methodologies
• Attention to detail
• Multi-tasking
• Strong analytical skills