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Data Scientist
Job Location:
Job Location:
Lexington County, SC - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
13 November 2026 (5 hours ago)
Posted:
Application Deadline:
10 February 2027
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Tittle: Data Scientist
Virtual role
Location: LexingtonMA.
The successful candidate will bring demonstrated experience developing production pipelines for semantic search including document processing embedding generation vector indexing and retrieval evaluation. This position combines approximately 60% data engineering and 40% applied data science.
The individual will work with an existing engineering team responsible for search strategy infrastructure and traditional keyword-based indexing. The role requires the ability to independently design and implement technical solutions while actively participating on a scrum team and collaborating on overall system architecture solutioning integration and continuous delivery.
Required Qualifications
2 years of experience with document chunking embedding generation vector indexing and semantic retrieval.
Experience evaluating benchmarking and fine-tuning embedding models for domain-specific applications.
2 years of experience with production vector databases or search platforms such as Qdrant Milvus Weaviate Pinecone Solr Elasticsearch or OpenSearch.
2 years of experience with NLP entity extraction entity resolution and integration of structured master data.
Hands-on experience developing MCP servers and tools integrating LLMs and building agent-based workflows.
Strong Python and SQL programming skills including data transformation API integration and service development.
Ability to independently design solutions evaluate alternatives and collaborate with multidisciplinary engineering teams.
U.S. citizenship and ability to obtain a security clearance are required.
Qualifications will be evaluated primarily on demonstrated technical accomplishments and relevant production experience rather than specific degrees or years of service.
Preferred Qualifications
Experience with Apache Solr Elasticsearch Qdrant or hybrid keyword/vector retrieval systems.
Experience developing REST APIs and services.
Experience implementing document-level access controls and preserving source-system permissions in data pipelines.
Familiarity with containerized applications automated testing CI/CD monitoring and production troubleshooting.
Experience designing multi-agent systems including tool orchestration context management and agent evaluation.
Experience with Kubernetes cloud-native data infrastructure or migration from on-premises systems.
Familiarity with JavaScript/TypeScript and modern AI application frameworks.
Experience working with enterprise identity systems organizational taxonomies and sensitive or access-controlled information.
Experience with search relevance evaluation reranking models and retrieval-augmented generation (RAG).
Expected Outcomes
During the first year the successful candidate will contribute to:
Establishing production semantic-search pipelines for existing research search applications.
Improving existing ingestion pipelines and integrating authoritative organizational master-data enrichment.
Developing and deploying MCP tools that make keyword and vector search capabilities available to internal applications and AI agents.
Implementing agent workflows that support search discovery and information retrieval.
Establishing repeatable methods for evaluating retrieval quality embedding models and pipeline reliability.
US Citizenship is required due to nature of the work.
Virtual role
Location: LexingtonMA.
The successful candidate will bring demonstrated experience developing production pipelines for semantic search including document processing embedding generation vector indexing and retrieval evaluation. This position combines approximately 60% data engineering and 40% applied data science.
The individual will work with an existing engineering team responsible for search strategy infrastructure and traditional keyword-based indexing. The role requires the ability to independently design and implement technical solutions while actively participating on a scrum team and collaborating on overall system architecture solutioning integration and continuous delivery.
Required Qualifications
2 years of experience with document chunking embedding generation vector indexing and semantic retrieval.
Experience evaluating benchmarking and fine-tuning embedding models for domain-specific applications.
2 years of experience with production vector databases or search platforms such as Qdrant Milvus Weaviate Pinecone Solr Elasticsearch or OpenSearch.
2 years of experience with NLP entity extraction entity resolution and integration of structured master data.
Hands-on experience developing MCP servers and tools integrating LLMs and building agent-based workflows.
Strong Python and SQL programming skills including data transformation API integration and service development.
Ability to independently design solutions evaluate alternatives and collaborate with multidisciplinary engineering teams.
U.S. citizenship and ability to obtain a security clearance are required.
Qualifications will be evaluated primarily on demonstrated technical accomplishments and relevant production experience rather than specific degrees or years of service.
Preferred Qualifications
Experience with Apache Solr Elasticsearch Qdrant or hybrid keyword/vector retrieval systems.
Experience developing REST APIs and services.
Experience implementing document-level access controls and preserving source-system permissions in data pipelines.
Familiarity with containerized applications automated testing CI/CD monitoring and production troubleshooting.
Experience designing multi-agent systems including tool orchestration context management and agent evaluation.
Experience with Kubernetes cloud-native data infrastructure or migration from on-premises systems.
Familiarity with JavaScript/TypeScript and modern AI application frameworks.
Experience working with enterprise identity systems organizational taxonomies and sensitive or access-controlled information.
Experience with search relevance evaluation reranking models and retrieval-augmented generation (RAG).
Expected Outcomes
During the first year the successful candidate will contribute to:
Establishing production semantic-search pipelines for existing research search applications.
Improving existing ingestion pipelines and integrating authoritative organizational master-data enrichment.
Developing and deploying MCP tools that make keyword and vector search capabilities available to internal applications and AI agents.
Implementing agent workflows that support search discovery and information retrieval.
Establishing repeatable methods for evaluating retrieval quality embedding models and pipeline reliability.
US Citizenship is required due to nature of the work.
