Data Scientist - Conversational AI Analytics - #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, Senior Director, Data Science - Head of Fair Lending Analytics - Fair & Responsible Banking Compliance Data is at the center of everything we do. As a startup, we disrupted the c…

• 13 days ago

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, 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)