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Principal Full-Stack Data Scientist – Foundational Models
Stitch FixCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying machine learning models, particularly in personalization and recommendation systems, while effectively communicating complex concepts to diverse stakeholders. Proficient in managing the full ML lifecycle and leveraging large-scale datasets for impactful solutions.
Highest-signal resume keywords
Machine Learning Model DevelopmentPython ProgrammingSQL and Spark ExperienceDeep Learning Frameworks (PyTorch, TensorFlow)Model Evaluation and Experimentation
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Design, develop, evaluate, and productionize machine learning models improving personalization and recommendation systems
- Advance foundational modeling capabilities, including 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 full ML 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 scalable, reliable, observable, cost-efficient production ML solutions
- Collaborate with Product, Engineering, and Data Science teams to translate downstream needs into reusable foundational ML capabilities
- Contribute to technical direction through design discussions, code reviews, research, prototyping, and best-practice development
- Communicate technical concepts, modeling approaches, tradeoffs, and recommendations to technical and non-technical stakeholders
- Mentor and collaborate with Data Scientists and engineers
Requirements
What you’ll need- Bachelor’s Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field required
- 8+ years of experience in design and deployment of machine learning solutions, ideally in personalization, such as recommendation systems, representation learning, or search
- Strong ability to architect technical solutions and write production-grade code in Python
- Ability to independently drive ambiguous machine learning problems from initial 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 modern deep-learning frameworks such as PyTorch or TensorFlow
- Strong understanding of model evaluation and experimentation, including offline evaluation, A/B testing, and translating model improvements into measurable product or business outcomes
- Ability to reason about production ML system tradeoffs, including model quality, latency, scalability, reliability, and computational cost
- Strong communication and collaboration skills, with the ability to influence technical decisions and communicate complex concepts to technical and non-technical stakeholders
- Intellectual curiosity and demonstrated ability to learn and apply new machine learning techniques to practical problems
Benefits
Comp & perks- Competitive salary
- Annual bonus eligibility
- New hire and ongoing restricted stock unit grants, depending on employee and company performance
- Medical benefits
- Dental benefits
- Vision benefits
- Other inclusive health and wellness benefits
- Comprehensive compensation package
- Equity
- Diverse and inclusive workplace
- Growth-oriented work environment
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningModel EvaluationData ExplorationProduction DeploymentA/B TestingRepresentation LearningRecommendation SystemsGenerative ApproachesMultimodal ModelingTechnical Solution Architecture
Soft Skills
Communication SkillsCollaboration SkillsMentoringIntellectual CuriosityInfluencing Technical Decisions
Tools & Technologies
PythonSQLSparkPyTorchTensorFlowDistributed Data-Processing ToolsBehavioral DatasetsProduct Datasets
Industry Keywords
PersonalizationRecommendation SystemsDeep LearningModel TradeoffsScalabilityReliabilityCost EfficiencyClient Impact