Principal Full-Stack Data Scientist, Foundational Models
New
S
Stitch FixPersonalization technology
Remote, USAFull-TimePrincipal
Salary$200,000 — $237,000 USD
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Job Details
- Experience
- 8+ years of experience in design and deployment of machine learning solutions
- Required Skills
- PythonSQLMachine LearningPyTorchSparkTensorflowA/B testing
Requirements
- Bachelor’s degree in Computer Science, Statistics, Physics, Mathematics, or a related quantitative field required.
- Master’s degree or PhD preferred.
- 8+ years of experience designing and deploying machine learning solutions.
- Experience in personalization, such as recommendation systems, representation learning, or search, is ideal.
- Ability to architect technical solutions and write production-grade code in Python.
- Ability to drive ambiguous machine learning problems from exploration and prototyping through production deployment, monitoring, iteration, and measurable impact.
- Experience working with large-scale datasets using SQL and distributed data-processing technologies such as Spark.
- Experience with deep-learning frameworks such as PyTorch or TensorFlow.
- Understanding of model evaluation and experimentation, including offline evaluation and A/B testing.
- Ability to reason about production ML tradeoffs including model quality, latency, scalability, reliability, and computational cost.
Responsibilities
- Design, develop, evaluate, and productionize machine learning models for personalization and recommendation systems.
- Advance client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.
- Explore and apply LLMs, deep learning, representation learning, multimodal modeling, and generative approaches.
- Own the machine learning lifecycle from problem formulation and data exploration through modeling, experimentation, deployment, monitoring, and iteration.
- Design offline evaluations and online experiments to measure model performance and client and business impact.
- Work with large-scale behavioral and product datasets using Python, SQL, and distributed data-processing tools.
- Build production-quality ML solutions with attention to scalability, reliability, latency, observability, and cost.
- Collaborate with Product, Engineering, and Data Science teams to develop reusable foundational ML capabilities.
- Contribute to technical direction through design discussions, code reviews, research, prototyping, and best practices.
- Mentor and collaborate with Data Scientists and engineers, and communicate technical concepts and tradeoffs to stakeholders.
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