Staff Applied Scientist

LinkedIn•Mountain View, CA

71d•Hybrid

About The Position

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 now looking for a talented and driven individual to accelerate our efforts and be a major part of our data-centric culture. It is expected that this person understands experimentation and/or machine learning techniques to be able to implement from scratch and have the ability to extend and enhance these techniques to specific applications like business problems. Successful candidates will exhibit technical acumen on inference and algorithms, and the business savviness to use these technical skills to drive better business decision-making. 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.

Requirements

Bachelor's Degree in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.
5+ years of industry or relevant academia experience
Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl)
Experience in applied statistics and statistical modeling in at least one statistical software package, (eg. R, Python)

Nice To Haves

7+ years of industry or relevant academia experience
MS or PhD in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.

Responsibilities

Work with a team of high-performing analytics, data science professionals, and cross-functional teams to identify business opportunities and develop algorithms and methodologies to address them.
Analyze large-scale structured and unstructured data.
Conduct in-depth and rigorous causal analysis and develop causal methodology and machine learning models to drive member value and customer success.
Develop methodologies to enhance LinkedIn's product and platform 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.
Improve LinkedIn's ability to measure and credibly speak to labor market trends and other economic phenomena.
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.

Benefits

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels.
LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $170,000 - $277,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.

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