Senior Software Engineer, Data Science

LinkedIn•Mountain View, CA

1d•Hybrid

About The Position

This role will be based in Mountain View, CA. 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. LinkedIn's Data Science team leverages big data to empower business decisions and deliver data-driven insights, metrics, and tools in order to drive member engagement, business growth, and monetization efforts. With over 1 billion members around the world, a focus on great user experience, and a mix of B2B and B2C programs, LinkedIn offers countless ways for an ambitious data engineer to have an impact and transform your career. We are looking for a talented and driven Sr Software Engineer, Data Science to help build data applications, platforms, and engineering foundations that enable LinkedIn’s Data Science organization to move faster and make better decisions. In this role, you will work closely with data science, product, engineering, marketing, sales, and operations partners to translate business and analytical needs into reliable data products, reusable tools, and scalable data systems. You will contribute to both application development and data engineering efforts, including building self-serve tools, diagnostic workflows, data pipelines, curated datasets, metrics foundations, and integrations with LinkedIn’s AI-powered data stack. Successful candidates will bring strong SQL and programming skills, practical software engineering judgment, and a passion for creating dependable data products that improve how teams discover, understand, and act on data.

Requirements

Bachelor's Degree in a quantitative discipline: Computer science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc.
3+ years of relevant industry or relevant academia experience working with large amounts of data
Experience with SQL/Relational databases
Background in at least one programming languages (e.g., R, Python, Java, Scala, PHP, JavaScript)

Nice To Haves

Master's Degree in Computer Science, Data Science, Software Engineering, Statistics, Applied Mathematics, or related field.
Communication
Decision Making
Data Science
Domain Knowledge and Skills
Strategic Thinking

Responsibilities

Designs and builds the architecture of both front-end and back-end for novel data applications aligned to open-ended or complex business problems/opportunities.
Develop and build methodologies, platforms, and tooling that support experimentation, causal inference, measurement, and diagnostics for complex product and business questions
Designs, builds, and customizes reusable tools, frameworks, dashboards, APIs, and integrations that make data applications and engineering solutions easier to adopt, operate, and extend across teams.
Conceptualizes, defines, and socializes foundational metrics to set organizational goals and scale with AI to democratize data-driven decision-making.
Leverages LinkedIn’s AI-enabled data stack and AI tools to accelerate discovery, design, development, testing, documentation, maintenance, and iteration of data products, applications, and pipelines.
Uses domain knowledge to evaluate design tradeoffs, identify risks, and communicate technical plans, progress, dependencies, and recommendations to cross-functional partners so data applications, pipelines, and tooling remain useful, reliable, and aligned to business needs.
Identifies gaps in existing data applications, tools, workflows, datasets, and pipelines, then proposes and implements improvements that reduce manual work, improve reliability, and increase reuse across projects.
Applies data science and software engineering best practices across the lifecycle of data applications and data engineering solutions and participates in code, data model, pipeline, and design reviews, providing constructive feedback and technical guidance to peers that improves maintainability, scalability, data quality, and user experience.
Support, monitor and continuously improve operational excellence practices for data applications and platforms, including reliability, observability, incident response, scalability, privacy, and production readiness.