LinkedIn
LinkedIn is the worlds largest professional network with a focus on belonging and skills development. This role sits in LinkedIns Data Science team, which uses data-driven insights to drive member engagement, business growth, and monetization. The role will tackle a wide range of technical challenges spanning products, engineering, research, data engineering, finance, and infrastructure, and will contribute to shaping product strategy through data-driven insights. The infrastructure focus includes datacenters, servers, network infrastructure, power systems, and foundational software platforms that power our products and services.
Responsibilities
Work with a team of analytics, data science professionals, and cross-functional partners to identify business opportunities and develop algorithms and methodologies to address them.
Analyze large-scale structured and unstructured data.
Develop methodologies to enhance LinkedIns operations and platform capabilities.
Apply technical expertise to forecasting and capacity planning to identify opportunities for improvement.
Engage with technology partners to build, prototype, and validate scalable tools/applications end to end (backend, frontend, data) for turning data into insights.
Promote and enable adoption of data science advances and elevate data science practices at LinkedIn.
Improve the ability to measure labor market trends and other economic phenomena with credibility.
Initiate and drive projects to completion independently.
Act as a thought partner to senior leaders to prioritize projects, provide recommendations, and evangelize data-driven decisions aligned with strategic goals.
Partner with cross-functional teams to initiate, lead, or contribute to large-scale strategic projects for the team, department, and company.
Provide technical guidance and mentorship to junior team members on solution design; lead code and design reviews.
Basic Qualifications
Bachelors Degree in a quantitative discipline (Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.). 5+ years of industry or relevant academic experience. Background in at least one programming language (e.g., R, Python, Java, Ruby, Scala/Spark, or Perl). Experience in applied statistics and statistical modeling in at least one statistical software package (e.g., R, Python).
Preferred Qualifications
7+ years of industry or relevant academic experience. MS or PhD in a quantitative discipline or other quantitative field. Experience in infrastructure planning, operations research, or a related field. Familiarity with cloud computing platforms and infrastructure management systems.
Suggested Skills
Statistical Modeling Forecasting Programming Data Analysis Infrastructure Planning
Compensation and Benefits
The pay range for this role is $170,000 $277,000. Actual compensation is based on factors such as skill set, depth of experience, certifications, and location. The total compensation package may include annual performance bonus, stock, benefits, and other incentive plans. For more information, visit the LinkedIn benefits page. Work Environment and Equal Opportunity
LinkedIn is committed to fair and equitable compensation practices and to offering an inclusive and accessible experience for all job seekers. We are an equal opportunity employer and consider qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender identity or expression, or any other legally protected class. If you need a reasonable accommodation during the job search or interview process, please contact accommodations@linkedin.com. Details about our pay transparency and privacy policies are available on our site. Job Postings and Locations
Recruitment notices and location-specific postings may appear in the description; the actual role location is hybrid (home and onsite on select days). This description does not constitute a job offer or contract and is intended for informational purposes only. #J-18808-Ljbffr
Basic Qualifications
Bachelors Degree in a quantitative discipline (Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.). 5+ years of industry or relevant academic experience. Background in at least one programming language (e.g., R, Python, Java, Ruby, Scala/Spark, or Perl). Experience in applied statistics and statistical modeling in at least one statistical software package (e.g., R, Python).
Preferred Qualifications
7+ years of industry or relevant academic experience. MS or PhD in a quantitative discipline or other quantitative field. Experience in infrastructure planning, operations research, or a related field. Familiarity with cloud computing platforms and infrastructure management systems.
Suggested Skills
Statistical Modeling Forecasting Programming Data Analysis Infrastructure Planning
Compensation and Benefits
The pay range for this role is $170,000 $277,000. Actual compensation is based on factors such as skill set, depth of experience, certifications, and location. The total compensation package may include annual performance bonus, stock, benefits, and other incentive plans. For more information, visit the LinkedIn benefits page. Work Environment and Equal Opportunity
LinkedIn is committed to fair and equitable compensation practices and to offering an inclusive and accessible experience for all job seekers. We are an equal opportunity employer and consider qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender identity or expression, or any other legally protected class. If you need a reasonable accommodation during the job search or interview process, please contact accommodations@linkedin.com. Details about our pay transparency and privacy policies are available on our site. Job Postings and Locations
Recruitment notices and location-specific postings may appear in the description; the actual role location is hybrid (home and onsite on select days). This description does not constitute a job offer or contract and is intended for informational purposes only. #J-18808-Ljbffr