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Data Scientist II​/Onsite​/FullStack

Job in St. Louis, Saint Louis, St. Louis city, Missouri, 63105, USA
Listing for: Motion Recruitment Partners, LLC
Full Time position
Listed on 2026-01-13
Job specializations:
  • IT/Tech
    Data Scientist, AI Engineer, Data Analyst, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 125000 - 150000 USD Yearly USD 125000.00 150000.00 YEAR
Job Description & How to Apply Below
Position: Data Scientist II/Onsite/ FullStack
Location: St. Louis

You’ll be joining a fast-moving team that builds intelligent, agentic analytics platforms used to solve real business problems across forecasting, custom modeling, pricing, and structured data validation. This team moves quickly turning proof-of-concepts around in 4-6 weeks and values both technical excellence and sound judgment over rote process. Your work will directly influence how companies operationalize machine learning and data science into strategic decisioning.

In this role you’ll own full-stack data science challenges end-to-end: from ideation and modeling strategy through implementation in production systems, integrating models into pipelines, and collaborating with engineering on deployable solutions. You’ll work on custom forecasting and pricing models, experiment with fine-tuning and agent-based approaches, and bring rigor to projects involving structured government form data and similar domains. The right person thrives in a dynamic environment, moves confidently without a script, and iterates quickly while still knowing when to slow down and ensure quality.

You

should have:
  • 3-7 years of hands-on data science experience, including real exposure to production stacks and deployed models
  • Strong grounding in custom modeling, forecasting, and pricing work
  • Comfortable building and validating models end-to-end, including data prep, validation, and deployment
  • A broad toolkit across statistical modeling, ML, and experimentation that is adaptable to multiple verticals
  • Curiosity, good judgment, and the confidence to push boundaries while shipping pragmatic results
  • Experience with model fine-tuning and an interest in autonomoagentic systems
  • The ability to move fast on POCs and collaborate effectively with engineering partners
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