Data Scientist
On-siteSeattle, Washington, United States
Job Summary
Design and deploy scalable machine learning and AI solutions that drive business value across the Revenue Intelligence team. Build production-ready ML pipelines from feature engineering through deployment and monitoring, integrating Large Language Models and Retrieval-Augmented Generation systems into enterprise workflows. Partner with stakeholders to translate complex challenges into actionable insights while collaborating with cross-functional teams on automation and process improvement. This hands-on role leverages modern frameworks like Streamlit and PySpark to create innovative, production-ready applications that shape the future of revenue intelligence.
Required Qualifications
- 4+ years of professional experience in Data Science, Machine Learning, AI Engineering, or a related field
- Strong programming skills in Python and SQL
- Experience designing and deploying scalable machine learning solutions, including feature engineering, model evaluation, deployment, and MLOps practices
- Hands-on experience with Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), or similar AI frameworks
- Solid understanding of data engineering concepts, including ETL/ELT pipelines, data modeling, governance, and storage optimization
- Experience with Databricks, PySpark, or distributed data processing platforms
- Familiarity with software engineering best practices, including APIs, version control, testing, CI/CD, and deployment workflows
- Experience with business intelligence and visualization tools such as Tableau, Power BI, or Qlik
- Excellent analytical thinking, problem-solving, communication, and technical writing skills
- Experience using Agile project management tools such as Jira, Confluence, or Trello
Desired Qualifications
- Experience developing AI copilots, autonomous agents, or conversational AI applications
- Exposure to cloud-native AI and machine learning platforms
- Familiarity with vector databases, embeddings, and semantic search technologies
- Experience working in Revenue Operations, Sales Analytics, or Business Intelligence environments
- Knowledge of future AI, OpenAI, or similar generative AI ecosystems
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