Tech Lead, GTM Applied AI and Analytics

Job Description

This role can be based in either our Sunnyvale, San Francisco, New York, or Chicago offices.

At Linked In, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a Linked In office on select days, as determined by the business needs of the team.

The Tech & Analytics team builds the analytical and automation foundation that powers Linked In's most important Go-to-Market decisions. We partner across Sales, Customer Success, Marketing, and Engineering to create a unified understanding of GTM performance. Our mission is to transform data into proactive insights and intelligent systems that guide Linked In's growth and efficiency.

As the Tech Lead for GTM Applied AI & Analytics, you are the technical authority and chief architect for the next generation of our GTM data solutions. This is a hands‑on leadership role for a "player‑coach" who will spend a significant portion of their time architecting solutions, writing production‑grade code, and mentoring a team of 3‑4 data scientists and analytics engineers.

You will be responsible for the end‑to‑end technical lifecycle of our most complex projects, from prototyping new AI‑driven concepts to deploying scalable, automated systems. You will combine the analytical depth of a principal data scientist with the strategic storytelling needed to influence GTM leadership. Your primary goal is to architect and build the agentic workflows, predictive models, and automated systems that will fundamentally change how our GTM teams operate.

• Architect & 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.

• 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, Num Py), analytics, and ML (e.g., scikit‑learn, Tensor Flow, PyTorch).
• Experience in SQLwith 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.

• MS or PhD in Computer Science, Statistics, or a related quantitative field.
• Experience with GenAI technologies and frameworks (e.g., Lang Chain, Llama Index, LLM APIs).
• Experience with MLOps principles and tools (e.g., MLflow, Kubeflow, Sage Maker, 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…