Data Scientist enhancing merchandising and inventory performance using machine learning techniques at Nordstrom. Collaborating with teams to develop data - driven solutions for better customer experiences.
About the role
- Senior Data Scientist building predictive and generative models at LegalShield. Designing infrastructure for AI solutions with compliance and performance optimization in mind.
Responsibilities
- Develop predictive and generative ML models to support acquisition, retention, personalization, and member engagement.
- Fine-tune and adapt Large Language Models (LLMs) for LegalShield-specific use cases, including call transcript analysis, voice-of-customer workflows, and content generation.
- Conduct experimentation, model evaluation, and rigorous validation to ensure accuracy, fairness, and business alignment.
- Partner with business teams to design experiments, analyze results, and translate insights into actionable strategies.
- Build and maintain infrastructure for deploying and scaling ML/ GenAI models.
- Develop APIs, MCP servers and microservices to serve models across internal and external applications.
- Translate prototypes into production-grade systems for business and member-facing teams.
- Implement CI/CD pipelines, containerization, and orchestration (Docker, Kubernetes).
- Deploy and manage ML workflow orchestration, feature stores, and model registries.
- Monitor and optimize performance, drift, latency, and cloud costs.
- Ensure compliance with SOC2, HIPAA, GDPR, and other data privacy/security standards.
- Develop internal AI tools that improve productivity and deliver measurable cost savings.
- Develop external, member-facing AI-powered applications that create new value and enhance customer experience.
- Build reusable platforms and tools that accelerate AI/ML adoption across LegalShield.
Requirements
- Advanced degree in Data Science, Statistics, Computer Science, or a related field (or equivalent practical experience).
- 7+ years’ experience in software engineering, Machine Learning, or Applied Data Science (Python preferred; APIs, microservices, distributed systems).
- Proven ability to take AI/ML solutions from prototype to production and scale adoption across teams.
- Hands-on experience with Large Language Models (LLMs) and retrieval-augmented generation (RAG) pipelines, LLM Fine-tuning and training.
- Familiarity with ML infrastructure: feature stores, model registries, or workflow orchestration tools.
- Experience with CI/CD, Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure).
- Understanding of compliance and data privacy standards, and ability to apply them in AI systems.
- Proven track record of deploying and operating ML/AI systems in production.
Benefits
- Commitment to Equal Opportunity
- Equal Opportunity Employer
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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