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.
Team blurb{{:}}
The GTM Data Science team operationalizes data and insights for LinkedIn’s go‑to‑market impact across Marketing, Sales, Pricing and Finance. We partner closely on multiple revenue generating initiatives. We also measure platform growth through rigorous metrics, experimentation, and enabling advanced data solutions. We organize and harmonize data at scale by building highly reliable, semantically rich data pipelines enabling data-driven decision making across the organization.
Responsibilities{{:}}
- Partner with product managers and go-to-market (GTM) teams to set up experimentation, support A/B testing, and experimentation readouts.
- Build data expertise, act as an owner for the company, and manage complex data systems for a product or group of products.
- Perform all necessary data transformations to serve products that empower data-driven decision making.
- Build and manage data pipelines, design and architect databases.
- Design, implement, integrate, and document performant systems or components for data flows or applications that power analysis at a massive scale.
- Ensure best practices and standards in the data ecosystem are shared across teams.
- Understand analytical objectives to make logical recommendations and drive informed actions.
- Be a self-starter; initiate and drive projects to completion with minimal guidance.
- Contribute to engineering innovations that fuel LinkedIn’s vision and mission.
Qualifications
Basic Qualifications{{:}}
- Bachelor's Degree in a quantitative discipline{{:}} Computer science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc.
- 5+ years of relevant industry or relevant academia experience working with large amounts of data
- Background in at least one programming language (e.g., R, Python, Scala)
- Experience in developing data pipelines using Spark and Hive.
- Experience with data modeling, ETL (Extraction, Transformation & Load) concepts, and patterns for efficient data governance. Experience with manipulating massive-scale structured and unstructured data.
- Experimentation experience
- Designing and performing data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop ecosystem understanding, and monitor current products or systems
- Evaluates A/B and causal tooling for discernable gaps and potentially partners with applied sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models.
- Translate business problems into data-verifiable hypotheses.
- Designing and performing data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop ecosystem understanding, and monitor current products or systems
- Evaluates A/B and causal tooling for discernable gaps and potentially partners with applied sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models.
- Translate business problems into data-verifiable hypotheses.
Preferred Qualifications{{:}}
- Experience working with large amounts of data
- MS or PhD in a quantitative discipline{{:}} statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
- Experience with A/B testing, experimentation, metric sensitivity, experimentation readout
- Experience in developing data pipelines using Spark and Hive.
- Experience with data modeling, ETL (Extraction, Transformation & Load) concepts, and patterns for efficient data governance. Experience with manipulating massive-scale structured and unstructured data.
- Experience with distributed data systems such as Hadoop and related technologies (Spark, Presto, Pig, Hive, etc.).
- Experience with either data workflows/modeling, front-end engineering, or back-end engineering.
- Deep understanding of technical and functional designs for relational and MPP Databases
- Experience in data visualization and dashboard design including tools such as Tableau, R visualization packages, D3, and other Javascript libraries, etc.
- Knowledge of Unix and Unix-like systems, git and review board.
Additional Information
Suggested Skills{{:}}
- Experimentation, A/B testing, experimentation readouts
- Data Pipeline
- ETL
- Data Manipulation
India Disability Policy
LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https{{:}}//legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_9-12-2023.pdf
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.
India Disability Policy
LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https{{:}}//legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_9-12-2023.pdf
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.