Staff Data Scientist, Applied Science

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.

LinkedIn's Data Science team leverages big data to develop data-driven solutions for LinkedIn's Infrastructure organization. LinkedIn's infrastructure is the backbone of our operations, encompassing data centers, servers, network infrastructure, power systems, and foundational software platforms that power all our products and services. A career at LinkedIn offers countless ways for an ambitious data scientist to have an impact.

This role involves guiding the Experimentation Platform team in developing a statistically correct, flexible, and powerful platform for online experimentation. You will be the statistical voice and technical lead, co-designing a new AI experimentation experience and backend, ensuring high-level functionality like hierarchical experimentation and statistical correctness guardrails. You will be in charge of developing and integrating a variety of experiment analysis techniques, including more powerful variance reduction methods, quantile treatment effect analysis, and cross-experiment analysis. Finally, you will help drive expansion of platform capabilities into additional experiment designs, such as cluster-randomized designs and budget split testing.

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.

Responsibilities:

• Co-design the new AI experimentation experience and the backend that powers it.

• Collaborate cross-functionally to identify business opportunities and develop algorithms and methodologies to address them.

• Analyze large-scale structured and unstructured data.

• Develop methodologies to enhance LinkedIn's online experimentation capabilities.

• 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.

• Initiate and drive projects to completion independently.

• 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, department, and company.

• Provide technical guidance and mentorship to junior team members on solution design as well as lead code/design reviews.

Basic Qualifications:

• MS or PhD in a quantitative discipline: Statistics, Economics, Applied Mathematics, Operations Research, Computer Science, Informatics, or other quantitative field relevant to the science of experimentation.
• Expertise in statistics, including analysis of bias and variance for experimental design and analysis approaches.
• Fluent in at least one programming language and a capable user of AI software engineering tools.
• Ability to establish requirements and design a new system for a broad set of users.
• 3+ years in a position that includes substantial technical leadership. 10+ years of total experience
  
  

Preferred Qualifications:

• Experience in infrastructure/platform development.
• Ability to collaborate with and shape technical solutions for a broad set of customers and stakeholders.
  
  

Suggested Skills:

• Structured thinking around bias
• Understanding of AI Experimentation
• Leadership
  
  

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