VECTOR DISTRIBUTION
VECTOR DISTRIBUTION

60 Vector Jobs Hiring in Virginia

AI Integration Engineer

Mclean, VA

$105K - $141K/yr

... such as Vector DBs, and multi-step reasoning traces. * Develop, manage, and optimize CI/CD ... Ability to manage and optimize distributed, high-performance computing environments, including ...

Mid-Level Java Developer

Reston, VA · On-site

$53 - $68.75/hr

Understand Call Center features; such as, Automatic Call Distributions (ACD), Call Vectors, and Vector Directory numbers (VDN). Ability to develop work and call flows Excellent customer service ...

Databricks, Apache Spark), including Delta Lake, distributed processing and performance optimization. * Experience supporting or integrating GenAI/ML workflows (e.g. feature engineering, vectors ...

Databricks, Apache Spark), including Delta Lake, distributed processing and performance optimization. * Experience supporting or integrating GenAI/ML workflows (e.g. feature engineering, vectors ...

Senior Data Engineer - Databricks

Mclean, VA · On-site +1

$125K - $165K/yr

Databricks, Apache Spark), including Delta Lake, distributed processing and performance optimization. * Experience supporting or integrating GenAI/ML workflows (e.g. feature engineering, vectors ...

Mid-Level Java Developer

Reston, VA · On-site

$53 - $68.75/hr

... Distributions (ACD), Call Vectors, and Vector Directory numbers (VDN). • Ability to develop work and call flows • Excellent customer service skills • Work with multiple network platforms, such ...

Lead Data Architect

Herndon, VA · On-site

$160K - $190K/yr

Implement vectorless and vectorization/embedding pipelines, vector store integrations, and ... Strong SQL skills; experience with distributed query/warehouse systems and parquet/AVRO/Delta ...

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Data Scientist - Conversational AI Analytics - #W2 Role

Interon IT Solutions

Mclean, VA • On-site

$50 - $55/hr

Contractor

Posted 16 days ago


Job description

#W2 Role
Title: Data Scientist – Conversational AI Analytics Location: Rockville, MD or McLean, VA (Hybrid) Only Local candidates who are in DC/VA/MD who can take Assessment before Submission and also required for F2F interview Overview We are seeking a highly analytical and technically skilled Data Scientist to help drive insights from conversational AI platforms and large-scale interaction data. This role focuses on extracting actionable intelligence from AI-generated conversations through advanced clustering, embedding analysis, LLM-assisted categorization, and analytics engineering. The ideal candidate combines expertise in machine learning, natural language processing (NLP), data engineering, and cloud-native analytics to uncover user behavior patterns, emerging topics, and operational insights that directly influence product strategy and platform evolution. This role partners closely with engineering, product, AI/ML, and business stakeholders to improve conversational AI experiences through data-driven decision making. Required Qualifications Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related technical discipline 5+ years of experience in data science, machine learning, NLP, or large-scale analytics engineering Strong proficiency in Python and data science ecosystems (Pandas, NumPy, Scikit-learn, PySpark, etc.) Experience with NLP, semantic embeddings, vector similarity, and clustering techniques Hands-on experience with LLMs, prompt engineering, and AI-assisted analytics workflows Experience building cloud-native analytics solutions in AWS, Azure, or GCP Strong SQL and data modeling skills Experience developing scalable analytical pipelines and automated workflows Ability to communicate complex analytical concepts to both technical and business audiences
Preferred Qualifications
Experience with conversational AI platforms, chatbot analytics, or AI interaction telemetry
Experience with vector databases, semantic search platforms, or retrieval systems
Familiarity with distributed data processing technologies such as Spark or Ray
Experience with orchestration frameworks such as Airflow or Step Functions
Knowledge of MLOps, experiment tracking, and model governance practices
Exposure to responsible AI, AI governance, or regulatory environments
Experience building dashboards and data visualizations using BI tools or custom analytics platforms
 
Technical Environment
Python, SQL, PySpark
NLP & Embedding Models
LLM Platforms & Prompt Engineering
AWS Cloud Services
Vector Search & Semantic Retrieval
Distributed Analytics & Data Processing
REST APIs & Data Pipelines
Data Visualization & Reporting Tools
CI/CD & Analytics Automation Frameworks(Edited)