Technical Data Analyst II

A client with Kforce is seeking a Technical Data Analyst II to join their team in Mountain View, CA. Summary: We're looking for an AI Full Stack Scientist - a rare hybrid who can design ML models, build the agentic infrastructure that powers them, and ship end-to-end AI-powered products with polished user-facing experiences. This role sits at the intersection of applied machine learning, platform engineering, and product development. You won't just build models and hand them off. You'll own the full lifecycle - from research and experimentation to production deployment to the interface a customer or internal user actually touches. Duties:

• Design, train, and deploy ML models for personalization, classification, ranking, and recommendation at scale (millions of records)
• Build and fine-tune NLP/embedding models (SBERT, Jina, transformer architectures) for semantic understanding and search
• Design and run rigorous A/B experiments - hypothesis formation, power analysis, metric selection, causal inference
• Evaluate and integrate emerging GenAI capabilities (LLMs, RAG pipelines, multi-modal models) where they create real value vs. where classical ML is the right tool
• Architect and build agentic AI systems - tool-using agents, MCP servers, multi-step reasoning workflows
• Design knowledge governance systems - schema design, semantic layers, async sync pipelines
• Build ML platform tooling that accelerates experiment velocity for the broader team (model selection, parallel execution, automated provisioning)
• Own the infrastructure decisions that are hard to reverse: interfaces, data contracts, governance models
• Build end-to-end AI-powered applications - from model inference backend to production frontend (React/Next.js, TypeScript)
• Create interactive dashboards, data tools, and internal products that make AI outputs accessible to non-technical stakeholders
• BS (MS a plus) in a quantitative field - Statistics, Economics, Computer Science, Engineering, or equivalent experience
• Experience in applied ML/AI with models deployed to production at scale
• Strong Python - ML frameworks (PyTorch/TensorFlow), data processing (PySpark, Pandas), API development (FastAPI/Flask)
• NLP & Embeddings - hands-on experience with transformer models, semantic search, fine-tuning, and retrieval-augmented generation
• Experimentation rigor - A/B testing design, statistical significance, causal reasoning
• Distributed computing - experience processing millions of records (Spark, GPU clusters, autoscaling)
• SQL mastery - complex queries, ETL pipelines, data modeling
• Frontend capability - React or Next.js, TypeScript, ability to build production-quality UIs (not just notebooks)
• Agentic AI systems - experience building or architecting tool-using agents, MCP servers, or multi-step AI workflows

• Experience with marketing personalization, recommendation systems, or customer segmentation
• Familiarity with MLOps tooling (MLflow, Weights & Biases, model registries)
• Experience building developer tools or internal platforms adopted by other teams
• Contributions to semantic layer or data governance systems
• Knowledge of real-time serving infrastructure (streaming, feature stores, low-latency inference)