Remote Applied Computer Science Jobs in California

Company Description

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.

Job Description

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 and Applied Science teams use data, experimentation, causal inference, machine learning, and AI to solve important product and business problems. With more than 1 billion members globally and products that span both consumer and enterprise use cases, LinkedIn offers scientists the opportunity to work on problems that directly shape member experience, customer value, growth, and monetization. 

We are looking for a strong individual contributor who can bring rigorous science to practical problems. In this role, you will work across areas such as experimentation, causal inference, prediction, measurement, optimization, personalization, and large-scale machine learning. You will be expected to go deep technically, build methods and models that fit real product needs, and turn promising ideas into tools, platforms, and systems that can be used at scale. 

The ideal candidate combines technical depth with strong product and business judgment. You should be comfortable developing methods from the ground up, adapting existing techniques to new problems, and working closely with cross-functional partners to make better decisions and deliver measurable impact. The work may span areas such as auctions, matching, market design, personalization, AI-powered product experiences, and other high-impact systems across LinkedIn. 

Responsibilities: 

• Support the identification of product and data solution improvement opportunities through structured analysis and investigation. 
• Leverage AI tools in day-to-day workflows to increase productivity 
• Conduct analyses, experiments, and modeling work to evaluate product performance and uncover actionable insights. 
• Research prior work, documentation, and relevant literature to inform analytical approaches. 
• Participate in reviews of methodologies, tools, and outputs to improve scientific rigor and consistency. 
• Build, evaluate, and refine machine learning models or statistical approaches using established data science best practices. 
• Implement data science solutions that improve data extraction, interpretation, and decision-making under guidance from senior team members. 
• Apply standards for accuracy, fairness, robustness, and reproducibility in analyses and modeling work. 
• Collaborate with Engineering, AI, Product, and other partners to understand business goals and translate them into analytical tasks and ML models. 
• Communicate findings, recommendations, and model results clearly to stakeholders.

Qualifications

Basic Qualifications:

• Bachelor's Degree in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.
• 1+ 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)

Preferred Qualifications:

• Doctorate in Statistics, Biostatistics, Applied Mathematics, Engineering, Operations Research, Economics, Informatics, Computer Science, Data Science or related field.
• BS and 2+ years of relevant work experience, MS and 1+ years of relevant work experience, or Ph.D. and 1+ in quantitative discipline.

Suggested Skills:

• Machine Learning
• Statistics
• Programming Languages

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $120,000 to $195,000. 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.

Additional Information

Equal Opportunity Statement 

We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.

If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation.
Fill out an Accommodation request here: https://app.smartsheet.com/b/form/b660a0327d044969abfd7a4e73d15c36

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

• Documents in alternate formats or read aloud to you
• Having interviews in an accessible location
• Being accompanied by a service dog
• Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

San Francisco Fair Chance Ordinance

Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.

Pay Transparency Policy Statement

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice and Compliance Posters for Job Candidates 

Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: https://www.linkedin.com/legal/candidate-portal.