Data Scientist – Conversational AI Analytics

Title: Data Scientist – Conversational AI Analytics
Location : Rockville, MD or McLean, VA
Target Start Date : ASAP
Type: contract
Pay Rate: DOE

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.

Key Responsibilities

Conversational Analytics & Insight Generation

• Design and implement scalable analytics pipelines to extract insights from large-scale conversational datasets generated by AI platforms and chat systems
• Develop LLM-driven facet extraction and categorization frameworks to classify conversations by intent, task type, topic, sentiment, and usage patterns
• Identify emerging themes, behavioral trends, and shifts in user interaction patterns over time
• Build dashboards, visualizations, and reporting artifacts that communicate analytical findings and business insights to technical and non-technical stakeholders
• Translate complex analytical outputs into actionable recommendations that improve platform usability, AI performance, and product strategy
• Partner with product and engineering teams to define KPIs, success metrics, and experimentation frameworks for conversational AI systems

Clustering, NLP & Machine Learning

• Design and optimize semantic clustering pipelines using unsupervised learning techniques such as HDBSCAN, hierarchical clustering, and similarity-based grouping
• Generate, evaluate, and operationalize text embeddings using modern embedding models for semantic search, clustering, and topic discovery
• Build multi-level clustering taxonomies that support both fine-grained and high-level categorization of conversations
• Evaluate clustering quality using silhouette analysis, persistence scoring, coherence metrics, and domain-informed validation techniques
• Experiment with dimensionality reduction approaches (UMAP, PCA, t-SNE) and distance metrics to improve cluster relevance and interpretability
• Apply NLP and LLM-based techniques for summarization, topic modeling, semantic labeling, and automated insight generation

LLM & Generative AI Enablement

• Design, refine, and optimize prompts for LLM-driven analysis workflows including facet extraction, summarization, categorization, and cluster labeling
• Evaluate LLM output quality and continuously improve prompt engineering strategies to increase accuracy and consistency
• Work with model serving infrastructure and embedding pipelines to support scalable inference workloads
• Explore and prototype emerging techniques in Generative AI, conversational analytics, autonomous insight generation, and retrieval-augmented workflows
• Collaborate with AI/ML engineers and platform teams to operationalize GenAI analytics capabilities

Data Engineering & Cloud Analytics

• Build and maintain scalable Python-based data pipelines for ingesting, transforming, and analyzing conversational datasets
• Develop cloud-native analytics workflows leveraging orchestration, serverless, and distributed compute services
• Work with object storage, search platforms, relational databases, and analytical query engines to manage analytical outputs and metadata
• Implement efficient batching, caching, incremental processing, and parallelization strategies for large-scale embedding and clustering workloads
• Maintain reproducible analytical environments, experiment tracking, and versioning for embeddings, prompts, models, and analytical artifacts
• Optimize data processing workflows for performance, scalability, reliability, and cost efficiency

Quality Engineering & Validation

• Develop evaluation frameworks to measure clustering quality, categorization accuracy, topic consistency, and analytical reliability
• Implement automated testing and regression validation to detect analytical drift or degradation in model outputs
• Validate analytical findings against known baselines, domain expertise, and business expectations
• Document methodologies, assumptions, model limitations, and analytical decision frameworks
• Support observability and monitoring for production analytics pipelines

Security & Compliance

• Follow organizational security standards and secure development practices for handling sensitive and regulated data
• Implement appropriate safeguards for personally identifiable information (PII), confidential datasets, and AI-generated outputs
• Participate in security reviews, threat modeling discussions, and risk mitigation activities related to analytics infrastructure and AI platforms
• Ensure compliance with data governance, auditability, and responsible AI practices

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

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