• Bachelor Degree in Computer Science, Math, Statistics, Engineering, a related quantitative field, or equivalent experience.
• 3+ years of experience in applying data science techniques to drive technical product development and decision-making
• Strong technical background in computer science, statistics, math, information science, or another quantitative field
• Fluency in at least one modern language useful for data processing (e.g. Python, Scala)
• Proficiency with relational data modeling and SQL
• Expertise in statistical methods and experimental design and analysis
• Experience with measurement techniques for search ranking/relevance/quality
• Experience with distributed data processing systems (e.g. Spark, Redshift)
• Background in advanced statistical modeling (e.g. GLM, mixed effects) and/or machine learning

• Designing and analyzing experiments to measure the impact of new product features
• Investigating high-level questions like “What are the collaborative patterns of the most successful teams using Asana?”
• Adding new metrics and aggregations to our data warehouse to make new classes of questions answerable
• Building models to predict the growth trajectory of different customer segments

The Data Science team works closely with our product, customer success, and sales teams; we work to make sure teams are asking the right questions and then find those answers. Our data informs us of the impact we are having on the world, and our team is crucial to architecting and building our data infrastructure. We also design and use data analysis tools that increase our data’s scalability, accuracy, and accessibility. We understand our company’s goals and proactively inform their direction with data so that our product can help more teams do great things.
As a Data Scientist at Asana, you’ll help us ask the right questions and answer them rigorously. You’ll work closely with our Product and Business teams to understand their goals and proactively inform their direction with data. You’ll keep taking on new responsibilities as you grow—from defining core metrics to building machine learning models and keeping the data flowing in our pipelines.