Tech Manager- GTM Applied AI & Analytics

LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

This role is based in the Bangalore office.

At LinkedIn, 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 LinkedIn office on select days, as determined by the business needs of the team.

As part of the Technology & Product Operations organization, you will drive high-impact initiatives that strengthen our product and GTM operating system—leveraging proprietary data to improve product quality, enable smarter tooling, and reduce friction across member, customer, and GTM experiences.

We are seeking a talented and driven technical leader who excels at delivering world-class AI-powered analytic and agentic solutions, actionable insights, and measurable business impact. You will design and implement data-driven initiatives that create both immediate value and long-term strategic advantage.

You bring strong business and technical acumen, product judgment, and are business-savvy with applied expertise in modern AI tools and techniques. You combine analytical rigor with a growth mindset to generate scalable, data-driven learnings. You are comfortable navigating large, complex, and ambiguous data ecosystems and influencing cross-functional stakeholders through strong relationship-building and collaboration.

This is a hands-on player-coach leadership role. You will architect solutions, write production-grade code using AI tools, and mentor operations and analytics colleagues on AI tooling and applications.

You will own complex initiatives — from prototyping AI-driven concepts to deploying scalable, automated systems. You will partner with R&D to build applications leveraging our internal platforms, as well as provide input to R&D on enhancements to our technical platforms and data infrastructure. Combining the analytical depth of a principal data scientist with executive-level storytelling, your primary goal is to architect and build agentic workflows, predictive models, and automated systems that fundamentally transform how operations teams operate.

Architect & Build

• Lead the hands-on design, development, and deployment of scalable data products, AI/ML models (e.g., member friction, customer impact, anomaly detection), and GenAI-powered agentic workflows.
  
  

Technical Strategy

• Define the technical roadmap and architecture for Technology & Product Operations org, including key decisions on frameworks, tooling, and practices. Partner with R&D to build applications leveraging our internal platforms, as well as provide input to R&D on enhancements to our technical platforms and data infrastructure
  
  

End-to-End Automation

• Write high-quality, production-ready Python and SQL to build and maintain automated data pipelines, advanced analytics, and insight-delivery systems.
  
  

Applied AI Integration

• Serve as the subject matter expert on applying modern AI, LLMs, and ML techniques (e.g., RAG, fine-tuning) to solve GTM business problems in partnership with Operations, Data Science and Engineering teams.
  
  

Technical Mentorship

• Mentor operations and analytics colleagues on AI tooling and applications, setting a high bar for technical rigor, code quality, and engineering best practices through a lead-by-example approach.
  
  

Executive Storytelling

• Translate 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 teams to operationalize and scale models from prototype to production, ensuring reliability and measurable business impact.
  
  

Basic Qualifications

• 8+ years of experience in data science, machine learning, or analytics engineering.
• 5+ years of experience in Python for data manipulation (pandas, NumPy), analytics, and ML (e.g., scikit-learn, TensorFlow, PyTorch).
• SQL experience with large-scale data warehouses (e.g., Presto, Trino, Spark SQL).
• 1+ years of experience with GenAI technologies and frameworks (e.g., LangChain, LLM APIs).
• 1+ years of architecting, building, and deploying machine learning models and/or automated data solutions in production environments.
• Bachelors in Computer Science, Statistics, Operations Research, Engineering, or a related quantitative field (or equivalent practical experience).
  
  

Preferred Qualifications

• MS or PhD in Computer Science, Statistics, or a related quantitative field.
• Experience with modern data stack and automation tools (e.g., Airflow, Databricks).
• Proven ability to lead ambiguous, complex technical initiatives from 0→1.
• Demonstrated experience influencing technical roadmaps in fast-moving environments.
• Resilient, resourceful, and self-directed with a strong bias for action.
• Passion for AI with a clear, strategic perspective on applying machine learning to drive business decisions.
  
  

Suggested Skills

• Python
• SQL
• Data Science
• Machine Learning
• Model Development & Deployment
  
  

India Disability Policy

LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https://legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_9-12-2023.pdf

Global Data Privacy Notice for Job Candidates ​

Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.