Tech Lead, GTM Applied AI and Analytics

Responsibilities

• Arcitect & Build: Lead the hands-on design, development, and deployment of scalable data products, AI/ML models (e.g., customer health, pipeline risk, propensity to buy), and GenAI-powered agentic workflows.
• Technical Strategy: Define the technical roadmap and architecture for the GTM Applied AI pillar, making key decisions on frameworks, tools, and MLOps practices.
• End-to-End Automation: Write high-quality, production-ready Python and SQL to build and maintain automated data pipelines, complex analytics, and insight-delivery systems.
• Applied AI Integration: Act as the subject matter expert in applying modern AI, LLMs, and ML techniques (e.g., RAG, fine-tuning) to solve concrete GTM business problems in partnership with central Data Science and Engineering teams.
• Technical Mentorship: Mentor and develop a team of data scientists and engineers, setting a high bar for technical rigor, code quality, and engineering best practices through a "lead-by-example" approach.
• Executive Storytelling: Translate highly complex technical concepts and model outputs into clear, concise, and actionable narratives for senior GTM and Operations leadership.
• Cross-Functional Partnership: Collaborate with Product, Engineering, and Data Science partners to operationalize and scale models from prototype to production, ensuring reliability and business impact.

Qualifications

• BA/BS degree in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Engineering) or equivalent practical experience.
• 10+ years of experience in data science, machine learning, or analytics engineering.
• Experience in Python for data manipulation (pandas, NumPy), analytics, and ML (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience in SQL with large-scale data warehouses (e.g., Presto, Trino, Spark SQL).
• Experience architecting, building, and deploying machine learning models or automated data solutions into a production environment.

Preferred Qualifications

• MS or PhD in Computer Science, Statistics, or a related quantitative field.
• Experience with GenAI technologies and frameworks (e.g., LangChain, LlamaIndex, LLM APIs).
• Experience with MLOps principles and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI) for model versioning, deployment, and monitoring.
• Experience with modern data stack and automation tools (e.g., Airflow, Databricks, dbt).
• Deep understanding of GTM financial and operational metrics (e.g., pipeline, ACV, margin, LTV, CAC, Customer Health).

Skills

• Suggested Skills: Python, SQL, Data Science, Machine Learning, Building and Deploying Models

Pay Range

$138,000 to $225,000