Symbotic is looking to solve real-world business problems by developing data pipelines and infrastructure for simulation, machine learning, and reinforcement learning to increase efficiency, speed, and flexibility in warehouse automation.
Requirements
- Experience with data transformation, ETL pipelines, and data wrangling using tools like Python, Pandas, NumPy, or SQL.
- Experience with ML and cloud computing models and tools for training in simulation, MLOps and model deployment pipelines.
- Direct experience with data science workflows: experimentation, statistical analysis, and visualization.
- Knowledge of real-time data processing (Kafka, Flink).
- Familiarity with BI tools (Tableau, Power BI, Looker).
- Familiarity with machine learning frameworks (scikit-learn, TensorFlow, PyTorch)
Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines.
- Develop and optimize data architectures for simulation, analytics and machine learning.
- Ensure data quality, integrity, and security across systems.
- Analyze large datasets to uncover trends, patterns, and actionable insights.
- Build, validate, and deploy predictive models and machine learning algorithms.
- Conduct A/B testing and statistical analysis to support decision-making.
- Visualize data and communicate findings to technical and non-technical stakeholders.
Other
- A bachelor’s or master’s degree in Computer Science, Data Science, Statistics, or related discipline provides the academic foundation for success.
- Minimum of 6 years of experience in data engineering and/or data science roles.
- Excellent verbal and written communication skills to effectively represent the derived results and to technical and non-technical audiences
- Ability to work independently and collaboratively in a fast-paced, iterative environment.
- Up to 10% of travel may be required. Employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations.
