Remote AI Data Scientist: NLP, Semantic Search & Analytics

System One

McLean (VA)

On-site

USD 120,000 - 150,000

Full time

11 days ago
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Benefits offered by this job

Health benefits
401(k) plan
Dental & Vision coverage

Job summary

System One is seeking an AI Data Scientist to partner with stakeholders and translate business needs into scalable data science solutions. You will design ML models for search, discovery, and insights across structured and unstructured data, with emphasis on NLP, semantic search, and entity resolution.

You will collaborate with data engineers to deploy models on Palantir Foundry, Databricks, and AWS-based platforms, while building evaluation and explainability frameworks for trusted AI outputs.

Qualifications

  • A degree in data science, CS, statistics or math.
  • 4+ years in data science, ML or analytics.
  • US citizenship and ability to obtain Public Trust clearance.
  • Experience with ML models including NLP and info retrieval.
  • Proficiency with Python and SQL; Databricks/Spark experience.

Responsibilities

  • Partner with stakeholders to define AI/analytics use cases.
  • Design models for search, discovery, and insights.
  • Develop NLP, semantic search, and entity resolution capabilities.
  • Work with production teams to deploy models on AWS/Spark platforms.
  • Create evaluation frameworks and explainability for AI outputs.
  • Contribute to analytics dashboards and client communications.

Skills

Python
ML
NLP
Databricks
AWS

Education

Bachelor/Master in CS/DS/Stats

Tools

Databricks
Spark
TensorFlow
PyTorch

Job description

System One is seeking an AI Data Scientist to partner with stakeholders and translate business needs into scalable data science solutions. You will design ML models for search, discovery, and insights across structured and unstructured data, with emphasis on NLP, semantic search, and entity resolution.

You will collaborate with data engineers to deploy models on Palantir Foundry, Databricks, and AWS-based platforms, while building evaluation and explainability frameworks for trusted AI outputs.

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