Senior Data Scientist

Job Summary

The Senior Data Scientist is a hybrid role at Legal Shield that blends data science, applied machine learning, and platform engineering to build and operationalize AI solutions capable of driving business impact. The position focuses equally on developing advanced predictive and generative models and on designing the scalable infrastructure required to deploy them securely and efficiently. As a key technical leader, you’ll work on end‑to‑end ML initiatives—from feature engineering and model experimentation to deploying production‑grade APIs and orchestration systems.

This includes fine‑tuning large language models for Legal Shield‑specific use cases, such as analyzing call transcripts, understanding customer sentiment, and generating personalized content. You’ll also ensure models meet high standards of accuracy, fairness, and compliance while translating insights into actionable business strategies.

Beyond modeling, this position plays a vital role in platform enablement. You’ll build the systems that make AI usable across Legal Shield, including containerized deployment pipelines, monitoring frameworks, and governance tools that meet SOC2, HIPAA, and GDPR standards. By developing both internal productivity tools and external member‑facing AI applications, you’ll help shape Legal Shield’s broader AI strategy—working closely with the Director of Data Science, the CTO, and cross‑functional business leaders to embed intelligent solutions across the organization.

In short, this role combines innovation with scalability—bridging the gap between research and production to make AI a core enabler of Legal Shield’s growth and customer experience.

• Performance Outcomes
• Modeling & Data Science
  
  • Develop predictive and generative ML models to support acquisition, retention, personalization, and member engagement.
  
  • Fine‑tune and adapt Large Language Models (LLMs) for Legal Shield‑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.
• Platform & Productionization
• 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.
• AI Tooling & Applications
• 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 Legal Shield.

• 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.

• Deep knowledge of ML frameworks (PyTorch,…