Symbotic is looking to solve real-world business problems in warehouse automation for increased efficiency, speed, and flexibility using data science and machine learning
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
- 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
