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Senior Staff Data Scientist- Flagship Data Science



Data Science
Sunnyvale, CA, USA
Posted on Thursday, June 13, 2024
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.

At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers both hybrid (from Bay Area, New York City, Seattle/Bellevue) or remote work options, meaning you can work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together, or this role can be performed remotely in most locations in the country of employment.

Our Flagship Data Science team is seeking a Senior Staff Data Scientist to work alongside the newly formed Applied Science team focused on building inference, algorithms, and models to identify and quantify complex cause and effect in the ecosystem. You’ll be responsible for leading impactful projects to contribute to LinkedIn’s Knowledge Marketplace strategy by solving unique challenges in this space e.g. using causal inference techniques to quantify causality between input and outcome metrics, measuring content creator and viewer network impact in experimentation, quantifying the supply and demand imbalance in the knowledge marketplace.

Partner with cross-functional teams to initiate, lead and drive to completion large-scale/complex strategic projects for teams, departments and the company via data driven solutions
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.
Work with a team of high-performing data science professionals, and cross-functional teams to identify business opportunities, optimize product performance or strategy
Analyze large-scale structured and unstructured data; develop deep-dive analysis and machine learning models to drive member value and customer success
Design and develop core business metrics, create insightful automated dashboards and data visualization to track them and extract useful business and product insights
Design and analyze experiments to test new product ideas or go to market strategies. Convert the results into actionable recommendations. Independently craft compelling stories; make logical recommendations; drive informed actions.
Engage with technology partners to build, prototype and validate scalable tools/applications end to end (backend, frontend, data) for converting data to insights.
Provide technical guidance and mentorship to junior team members on solution design as well as lead code/design reviews
Drive org-wide impact by shaping product and business strategy through data-centric storytelling and presentations
Act as a champion for a data-driven culture, evangelizing best practices both with LinkedIn and among the local and global data science community.

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:
•Data Science
•Causal Inference

LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $157,000 to $257,000. 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

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LinkedIn is committed to diversity in its workforce and is 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 an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: Please reference and for more information.

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