Staff Data Scientist
New
T
ToastRestaurant Technology
Remote, USAFull-TimeStaff
SalaryZone A $170,000 — $272,000 USD, Zone B $148,000 — $237,000 USD, Zone C $133,000 — $213,000 USD
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonSQLMachine LearningPyTorchTensorflowscikit-learnMLOps
Requirements
- 7+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.
- Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.
- Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.
- Experience with distributed data processing and training, real-time inference, and ML Ops frameworks.
- Prior experience mentoring other data scientists or acting as a tech lead.
- Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.
- Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
- Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.
- Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.
- Excellent communication skills and the ability to influence both technical and non-technical stakeholders.
- Strong business acumen with the ability to align technical solutions with company goals.
- Experience building services on top of LLMs in a large scale production environment.
Responsibilities
- Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.
- Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.
- Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.
- Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.
- Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.
- Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.
- Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building.
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