Senior Data Scientist (hybrid)

Founded in 1993 and headquartered in Austin, Texas, Baxter Planning is a dynamic and rapidly global growing company setting the standard for SaaS in the service supply chain space. With a world-class SaaS platform, BaxterPredict, and unique PaaS (Planning as a Service) solutions, our technologies and professionals help enterprise customers plan billions in service parts inventory every day, enabling them to drive inventory reduction while delivering improvements in service levels.

Baxter Planning combines the flexibility of a start up with the stability of an established company, leveraging more than 30 years of industry expertise to support a rapidly growing customer base. We offer excellent benefits, an exciting and supportive work environment, and work-life balance. 

As a Senior Data Scientist, you will lead the development of advanced machine learning models—particularly for time series forecasting—using algorithms such as ARIMA, LSTM, XGBoost, and other state-of-the-art techniques. You will design and deploy ML engines as microservices in cloud environments (primarily AWS), leveraging services like SageMaker, Lambda, Glue, and EC2. In this role, you’ll also help build scalable ML infrastructure using Infrastructure as Code tools such as Terraform or CloudFormation, and contribute to robust model lifecycle management with MLOps tools like MLflow, DVC, or SageMaker. This is a hands-on, production-focused role that blends strong modeling skills with engineering best practices to deliver impactful, data-driven solutions.
Responsibilities:

• Develop end to end ML solutions using Python.
• Create and perform experiments to evaluate innovative solutions using AI and ML.
• Create and maintain e2e ML pipelines
• Work with costumers and product teams to create innovative solutions using ML models

Qualifications and skills needed:

• Technical degree: engineering, computer science, physics, Mathematics, quantitative economics, etc.
• 5+ Years of experience in data science
• Advanced Python programming
• Experience in modeling time series forecasting solutions
• Version control (git)
• Advanced SQL
• Proficiency in development ML models
• Solid experience using data/ML libraries like Pandas, SKlearn, Darts, TensorFlow, XGBoost, etc.