Senior GenAI Data Scientist

Description • We’re seeking an experienced contractor to architect, build, and productionize GenAI data science workflows that transform enterprise data into actionable business intelligence. This role sits at the intersection of generative AI, data engineering, and business analytics, requiring both deep technical expertise and the ability to collaborate effectively with business stakeholders. • You’ll be working primarily on GenAI applications for sales intelligence, leveraging call transcripts and business data to deliver high-impact use cases in production. Duties • GenAI Engineering & Production • Design and implement end-to-end GenAI workflows that integrate enterprise data sources (accounting/finance systems, sales call transcripts, CRM data) • Build and deploy agentic AI workflows using frameworks like LangGraph, LangChain, or similar orchestration tools • Implement comprehensive observability, evaluation frameworks, and guardrails for production GenAI systems • Establish best practices for prompt engineering, retrieval-augmented generation (RAG), and model selection • Critically evaluate use cases to determine when GenAI is (and isn’t) the appropriate solution • Data Engineering & Architecture • Design and implement data models including star schemas, slowly changing dimensions (SCD Type 2), and fact/dimension tables • Write complex SQL queries to extract, transform, and analyze data from enterprise databases and data warehouses • Build robust data pipelines using Apache Airflow for workflow orchestration • Process and transform data using Python, pandas, and numpy • Ensure data quality, governance, and compliance standards are met • Business Intelligence & Stakeholder Management • Translate business requirements into technical solutions, particularly around sales metrics, KPIs, and performance analytics • Work directly with business stakeholders to gather requirements, manage expectations, and communicate timelines • Provide strategic guidance on what’s feasible, what’s valuable, and what trade-offs exist • Deliver clear documentation and presentations for both technical and non-technical audiences Requirements • Technical Expertise • GenAI Proficiency: Deep hands-on experience with LLM applications, including observability tools, evaluation frameworks, and safety guardrails • Agentic AI: Demonstrated experience building multi-agent or agentic workflows using LangGraph or similar frameworks • LLM Fundamentals: Strong understanding of how LLMs work, their capabilities and limitations, context windows, tokenization, embeddings, and fine-tuning • AI-Assisted Development: Active user of GenAI coding tools (Cursor, GitHub Copilot, Codex, Gemini Code Assist, etc.) with proven ability to accelerate development • SQL Mastery: Expert-level SQL skills including complex joins, window functions, CTEs, query optimization, and performance tuning • Data Engineering: Expert knowledge of dimensional modeling (star schemas, SCD Type 2), data warehouse concepts, and ETL/ELT patterns • Python Stack: Advanced proficiency in Python, pandas, numpy, and related data science libraries • Workflow Orchestration: Production experience with Apache Airflow or similar orchestration platforms • Enterprise Data Integration: Experience working with structured data from ERP, CRM, and financial systems • Business & Domain Knowledge • Strong grasp of sales operations, pipeline metrics, conversion funnels, and revenue analytics • Understanding of key business metrics and KPIs across sales, finance, and operations • Ability to identify high-value use cases and prioritize based on business impact • Experience analyzing sales conversations and extracting actionable insights • Soft Skills & Work Style • Communication: Excellent written and verbal communication skills; ability to explain complex technical concepts to non-technical stakeholders • Stakeholder Management: Proven track record managing business partner relationships, setting realistic expectations, and delivering on commitments • Independence: Self-directed and able to own projects end-to-end with minimal supervision • Pragmatism: Bias toward shipping working solutions; comfortable with iteration and incremental delivery • Problem Solving: Strong analytical and debugging skills; resourceful when facing ambiguous challenges • Nice to Have: • Experience with vector databases • Knowledge of cloud platforms (AWS, GCP, Azure) and their AI/ML services • Experience with dbt (data build tool) for analytics engineering • Experience with streaming data and real-time processing • Background in conversation intelligence or speech-to-text applications • Understanding of privacy, security, and compliance requirements for AI systems (SOC 2, GDPR, etc.) • Previous experience in a startup or fast-paced environment • Familiarity with modern data warehouse solutions (Snowflake, Hive) Compensation • $116.18 – 124.14/hr W—2