Senior Staff Applied Scientist, Infrastructure DS

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, a career at LinkedIn offers countless ways for an ambitious data scientist to have an impact.

We are seeking a talented and driven individual to accelerate our efforts and contribute to LinkedIn's data-centric culture. In this role, you will tackle a wide range of technical challenges spanning products, engineering, research, data engineering, finance, and infrastructure. Leveraging data and analysis, you will address critical challenges within our infrastructure organization, shaping product strategy and guiding investment decisions with data-driven insights.

LinkedIn's infrastructure is the backbone of our operations, encompassing data centers, servers, network infrastructure, power systems, and foundational software platforms that power our products and services.

As an Applied Scientist in LinkedIn Infrastructure, you’ll develop and apply rigorous quantitative methods to optimize the systems that power our global platform. Your work will focus on forecasting, inference, and optimization across data centers, compute, network, and power systems, while also designing signal-driven monitoring and alerting models to detect risk, anomalies, and degradation in real time, and building measurement frameworks that quantify and attribute infrastructure cost, performance, and reliability to different products and lines of business, where accuracy, resilience, and scale are critical.

Responsibilities:

• Provide direction and oversight for in-depth and rigorous causal inference methodology and machine learning models to drive member value; design and conduct rigorous A/B tests, refine experimentation methodologies to identify and quantify complex cause and effect in the ecosystem and to continuously drive member values.
• Guide the working team to explore vast datasets to discover relevant features and attributes that can improve the performance of existing models. Extract valuable information from unstructured data sources and apply feature engineering techniques to enhance model effectiveness. Continuously optimize and fine-tune models to meet business objectives and user expectations.
• Engage with technology partners to build, prototype and validate scalable tools/applications end to end (backend, frontend, data) for converting data to insights
• Promote and enable adoption of technical advances in Data Science; elevate the art of Data Science practice at LinkedIn.
• Act as a thought partner to senior leaders to prioritize/scope projects, provide recommendations and evangelize data-driven business decisions in support of strategic goals
• Partner with cross-functional teams to initiate, lead or contribute to large-scale/complex strategic projects for team, org, and company