Exp: 3 - 8 years
CTC: 12 - 42 LPA
Preferred: Talents from eComms/Product/BFS
• Work with a team of high-performing data science professionals, and cross-functional teams to identify business opportunities, optimize product performance or go to market strategy.
• Build data expertise, act like an owner for the company and manage complex data systems for a product or a group of products.
• Performing all of the necessary data transformations to serve products that empower data-driven decision making.
• Establishing efficient design and programming patterns for engineers as well as for non-technical partners.
• Designing, integrating and documenting technical components for data flows or applications that perform analysis at a massive scale.
• Ensuring best practices and standards in our data ecosystem are shared across teams.
• Understand the analytical objectives to make logical recommendations and drive informed actions.
• Engage with internal platform teams to prototype and validate tools developed in-house to derive insight from very large datasets or automate complex algorithms.
• Initiate and drive projects to completion with minimal guidance.
• Contribute to engineering innovations that fuel LinkedIn’s vision and mission.
• Bachelor or higher degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
• 3+ years relevant industry or relevant academia experience working with large amounts of data
• Experience with SQL/Relational databases
• 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 data modelling, ETL (Extraction, Transformation & Load) concepts, and patterns for efficient data governance.
• Understanding of technical and functional designs for relational and MPP Databases, Reporting and Data Mining systems.
• Experience working with databases that power APIs for front-end applications.
• Knowledge of Unix and Unix-like systems, git and review board.
• Masters or Ph.D. degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
• Bachelors with 10+ years or Masters with 6+ years or Ph.D. with 4+ years of industry experience
• Experience in developing data pipelines using Spark and Hive.
• Experience with either data workflows/modeling, front-end engineering, or back-end engineering.
• Strong communication skills, with the ability to synthesize, simplify and explain complex problems to different audiences.
• Experience in either the front-end or back-end development of data-powered applications.
• Experience working in the product, sales, or marketing analytics domains.