Senior Associate , Decision science

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.

The AI team within Tech COE organization is responsible for building the ultimate go-to-market engine to connect our solutions with customer needs at scale. As a Senior Associate Decision Science, you will be partnering with stakeholders to crack the most important challenges to drive operational excellence and ultimately increase customer value.

This role focuses on building and deploying AI and GenAI solutions that turn complex data into actionable business insights, improve forecasting, and drive operational efficiency. It involves solving advanced analytical problems, creating intelligent systems for sales teams, and partnering cross-functionally to scale data-driven processes and innovation.

The ideal candidate should have a strategic mindset and strong communications skills to collaborate with cross-functional stakeholders and drive critical business decisions. The candidate should also be able to handle highly sensitive, confidential, and non-routine information, have high attention to detail, be open-minded to challenge the status quo and work in a rapidly changing organization in close collaboration with business partners.

Responsibilities

• Lead the end-to-end lifecycle of strategic AI/ML initiatives, from problem definition and solution design to production deployment and business adoption.
• Partner with Product, Engineering, Sales, and business stakeholders to identify high-impact opportunities and translate ambiguous business problems into scalable AI-driven solutions.
• Design, develop, evaluate, and productionize machine learning, deep learning, and Generative AI models, including agentic AI workflows where appropriate.
• Own technical solution architecture, model selection, experimentation strategy, and production deployment while ensuring scalability, reliability, and maintainability.
• Drive AI innovation by identifying opportunities to leverage LLMs, AI agents, recommendation systems, optimization techniques, and predictive modelling to improve business outcomes.
• Collaborate closely with Engineering and Infrastructure teams to build robust ML pipelines, automate model retraining, monitoring, and continuous evaluation.
• Define success metrics, conduct experiments, analyse outcomes, and communicate insights and recommendations to senior leadership.
• Mentor junior engineers through technical guidance, code reviews, and best practices in AI/ML development.
• Influence technical direction across multiple projects while driving engineering excellence and reusable AI platforms.

Basic Qualifications

• Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
• 7+ years of industry experience applying machine learning and statistical techniques to solve complex business problems.
• Programming skills in Python with experience building production-grade ML applications.
• Experience building scalable ML pipelines and integrating models into production systems.
• Experience working with large-scale structured and unstructured datasets using Spark, SQL, or distributed computing frameworks.
• Communication skills: ability to influence technical and non-technical stakeholders.

Preferred Qualifications

• Experience building production-grade Generative AI applications using LLMs, Retrieval-Augmented Generation (RAG), AI agents and orchestration frameworks.
• Experience in deploying machine learning models in Azure cloud.
• Experience with distributed data systems such as Hadoop and related technologies (Spark, Presto, Pig, Hive, etc.)
• Background in any one of programming language (C#, Java, PHP, JavaScript)
• Experience designing multi-agent systems and autonomous AI workflows.
• Hands-on experience with modern AI frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or equivalent.
• Deep understanding of technical and functional designs for relational and MPP Databases.
• Familiarity with CI/CD, MLOps, model monitoring, observability, and production AI governance.
• Experience mentoring scientists and leading cross-functional technical initiatives.
• Publications, patents, or contributions to open-source AI projects are a plus.

Suggested Skills

• Machine Learning & Deep Learning
• AI Model Deployment
• Distributed Data Systems
• Mentoring
• Stakeholder Management

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