CSG is a leader in innovative customer engagement, revenue management and payments solutions that make ordinary customer experiences extraordinary. Our cloud-first architecture and customer-obsessed mindset help companies around the world launch new digital services, expand into new markets, and create dynamic experiences that capture new customers and build brand loyalty. For over 40 years, CSG’s technologies and people have helped some of the world’s most recognizable brands solve their toughest business challenges and evolve to meet the demands of today’s digital economy with future-ready solutions that drive exceptional customer experiences. With more than 5,000 employees in over 20 countries, CSG is the trusted technology provider for leading global brands in telecommunications, retail, financial services and healthcare. Our solutions deliver real world outcomes to more than 900 customers in over 120 countries.

Job description

We are looking for a Senior Data Scientist to lead the design and implementation of end-to-end data science projects. This role involves developing large-scale data pipelines, leveraging AWS Glue and PySpark, and utilizing Amazon SageMaker for machine learning model development and deployment. You will also be responsible for monitoring production models using AWS CloudWatch and automating deployment processes to enhance operational efficiency.

Key Responsibilities

Design and implement end-to-end data science solutions.
Develop large-scale data pipelines using AWS Glue and PySpark.
Build, train, and deploy ML models using Amazon SageMaker.
Monitor and maintain production models with AWS CloudWatch.
Automate deployment processes to streamline MLOps workflows.
We are looking for candidates who have:
Bachelor’s degree in Computer Science, Data Science, Statistics, or related fields.
5+ years of experience in data science roles.
Proficiency in Python and PySpark.
Hands-on experience with AWS Glue, SageMaker, and CloudWatch.
Strong problem-solving and analytical skills.
Experience with CI/CD pipelines for data workflows.
Knowledge of MLOps best practices.