Senior Staff Applied Scientist, Infrastructure DS at LinkedIn

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Jun, 26

Salary

326000.0

Posted On

20 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Causal Inference, Machine Learning, A/B Testing, Experimentation, Data Analysis, Feature Engineering, Optimization, Forecasting, Inference, SQL, Python, R, Scala, Data Visualization, Mentoring, Communication

Industry

Software Development

Description

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. Join us to transform the way the world works. Job Description 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 Qualifications Basic Qualifications: B.S. Degree in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc. 5+ years experience with SQL or relational database query performance, and at least one programming language (e.g., R, Python, Scala) 2+ years experience in an architect or technical leadership position Preferred Qualifications: 10+ years of overall experience with at least 5+ of those years leading teams technically Experience influencing strategy through data-centric presentations Experience in applied statistics and statistical modeling in at least one statistical software package Experience telling stories with data and visualization tools Experience running platform experiments and techniques like A/B testing Ability to work with multiple stakeholders, understand the product priorities, think with the big picture and solve core problems in the most efficient way Experience with manipulating massive-scale structured and unstructured data Proven record writing and optimizing code with high levels of craftsmanship, and coaching others to improve technical outputs Experience mentoring other data scientists in an official or unofficial capacity Excellent communication skills, with the ability to synthesize, simplify and explain complex problems to different types of audience, including executives and compile compelling narratives Demonstrated thought leadership; experience publishing publicly visible research papers and/or speaking at conferences. MS or PhD in a quantitative discipline: Statistics, Economics, Applied Mathematics, Operations Research, Computer Science, Informatics, Engineering, etc. Suggested Skills: Statistical Modeling Forecasting Programming Data Analysis Infrastructure Planning You will Benefit from our Culture 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 $198,000 - $326,000. 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 at [email protected] and describe the specific accommodation requested for a disability-related limitation. 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 for Job Candidates Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal. Workplace Type: Hybrid Career Track & Grade: IC5/10 Department: Engineering

Responsibilities

This role involves providing direction for causal inference methodology and machine learning models to drive member value, alongside designing and conducting rigorous A/B tests and refining experimentation methodologies. Responsibilities also include engaging with technology partners to build and validate scalable data-to-insight tools and acting as a thought partner to senior leaders.