Job Description

Data Scientists on the Orders Analytics team are embedded within product teams and leverage engineering, analytics, statistics, and machine learning to empower data-driven decision making in product development. Data Scientists support Square’s mission of economic empowerment, by using data to build understanding and empathy with our customers, enabling us to build a remarkable product experience. 

You will:

• Partner with the product stakeholders to identify, prioritize, and answer the most important questions where analytics will have a material impact and produce actionable insights
• Develop tools & resources to empower data access and self-service so your expertise can be leveraged where it is most impactful
• Apply a diverse set of tactics such as statistics, quantitative reasoning, and machine learning; discerning where simple analytics solutions (e.g. a quick visualization) are preferable to complex solutions (e.g. machine learning).
• Communicate analysis and decisions to high-level stakeholders in verbal, visual, and written media
• Contribute to the data strategy of the platform and influence stakeholders to make well-informed architecture and design decisions that affect data at Square

Qualifications

You have:

• 3+ years of product-focused data science experience or equivalent
• Fluency in SQL
• Experience performing data analysis and machine learning using Python or R
• Experience using statistics to inform decision making
• Ability to understand complex business and data systems; versatility and willingness to learn new technologies on the job

Even better:

• A degree (B.S., M.S., PhD.) in Mathematics, Statistics, Computer Science, Physical Sciences, Economics, or a related technical field
• Experience with a BI tool such as Looker or Tableau 
• Experience with data warehouse design, development and best practices

Technologies we use and teach:

• SQL (Snowflake)
• Python (numpy, pandas, scikit-learn, etc.)
• Machine Learning (e.g. regression, ensemble methods, neural networks, etc.)
• Statistics (Bayesian methods, experimental design, causal inference)
• Snowflake
• Google Cloud Platform: BigQuery, Dataflow, and Composer
• Airflow & Prefect for ETL 
• Looker, Amplitude, & Tableau for data visualization