Data Scientist

Key Responsibilities:

Conversation Analytics & Insight Generation

Design and implement analytics pipelines that extract meaningful patterns from large-scale chat conversation data

Develop facet extraction approaches using LLMs to categorize conversations by request type task performed and topic discussed

Build dashboards and reporting artifacts that communicate usage trends emerging topics and user behavior to stakeholders

Identify and quantify shifts in conversation patterns over time to inform product roadmap and content strategy

Translate analytical findings into actionable recommendations for platform improvement

Clustering & Unsupervised Learning

Architect and optimize hierarchical clustering pipelines using density-based algorithms (e.g. HDBSCAN) to group conversations by semantic similarity

Generate and manage text embeddings at scale using embedding models for downstream clustering and similarity tasks

Design multi-level clustering strategies that produce both granular groupings and higher-order category taxonomies

Evaluate cluster quality using persistence metrics silhouette analysis and domain-informed validation

Experiment with clustering parameters distance metrics and dimensionality reduction techniques to improve grouping coherence

Data Engineering & Pipeline Development

Build and maintain data pipelines using Python for ingesting transforming and analyzing conversation datasets

Develop automated workflows using cloud-native orchestration and compute services to run analytics at scale on scheduled cadences

Work with object storage search engines and relational databases to store and query analytical outputs

Implement caching batching and incremental processing strategies to handle large embedding and clustering workloads efficiently

Maintain reproducible analysis environments and version analytical artifacts (models cluster outputs embeddings)

LLM-Assisted Analysis

Design and refine LLM prompts for facet extraction cluster labeling and conversation summarization

Evaluate LLM output quality for analytical tasks and iterate on prompt strategies to improve accuracy

Leverage model infrastructure for embedding generation and LLM inference

Explore emerging techniques in LLM-driven data analysis topic modeling and automated insight generation

Quality & Testing

Develop evaluation frameworks to measure clustering quality facet extraction accuracy and analytical pipeline correctness

Build automated regression tests to detect drift in clustering outputs or degradation in categorization quality

Validate analytical results against known baselines and domain expertise

Document methodologies assumptions and limitations of analytical approaches

Security & Compliance

Assist with adherence to technology policies and comply with all security controls

Implement secure coding practices particularly in handling personally identifiable information (PII) and sensitive data

Participate in threat modeling and security discussions for API and infrastructure components

Understand and apply organizational security standards and best practices