Founded in 1993 and headquartered in Austin, Texas, Baxter Planning is a rapidly growing global company setting the standard for SaaS in the service supply chain space. With its world-class SaaS platform, BaxterPredict, and unique PaaS solutions, the company helps enterprise customers manage billions in service parts inventory daily, driving inventory reduction and improving service levels.
Baxter Planning combines startup agility with the stability of an established firm, leveraging over 30 years of industry expertise. We offer excellent benefits, an exciting work environment, and promote work-life balance.
We are seeking a Senior Data Scientist to lead the development of advanced machine learning models, especially for time series forecasting, using algorithms like ARIMA, LSTM, XGBoost, and other cutting-edge techniques. The role involves designing and deploying ML engines as microservices in cloud environments (primarily AWS), utilizing services such as SageMaker, Lambda, Glue, and EC2. You will also help build scalable ML infrastructure with Infrastructure as Code tools like Terraform or CloudFormation and contribute to robust model lifecycle management with MLOps tools such as MLflow, DVC, or SageMaker. This is a hands-on, production-oriented role that combines 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 AI and ML solutions.
* Create and maintain end-to-end ML pipelines.
* Collaborate with customers and product teams to develop innovative ML-based solutions.
Qualifications and Skills:
* Technical degree in engineering, computer science, physics, mathematics, or quantitative economics.
* 5+ years of experience in data science.
* Advanced proficiency in Python programming.
* Experience in modeling time series forecasting solutions.
* Proficiency with version control systems like Git.
* Advanced SQL skills.
* Proficiency in developing ML models.
* Solid experience with data/ML libraries such as Pandas, scikit-learn, Darts, TensorFlow, XGBoost, etc.
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