Data Scientist, Lightspeed

• Proficiency in SQL and a scripting language (preferred python) in a data science context
• An entrepreneurial mindset and significant experience running experiments are key to this role
• Extensive experience using data to solve business problems
• Ability to break down complex projects into smaller work packages
• Experience with a data warehouse (we use BigQuery)
• Experience in performing data profiling, data mapping, and data quality validation
• Experience with source control using Git or equivalent
• Excellent communication skills

Desirable

• Lightspeed is growing at a fast pace and we’re looking for a passionate data scientist to join the Data Foundation team within the Wholesale & Inventory team
• The primary function for this position will be to establish and maintain insights for our corporate partners and brands
• You are inspired by generating value by leveraging data within the organization. As a leading member of this team, you will be responsible for developing models, analytics and insights that will change how our customers do business
• Work closely with our product team to deliver models that we can productize and turn into prescriptive analytics and recommended actions
• Collaborate with software engineers, data analysts and data architects, to design and train data models for downstream use
• Extract, cleanse and transform raw data into business logic to ensure the right data gets to the right people at the right time
• Use advanced programming languages, statistical models/algorithms and machine learning to identify trends and insights from data
• Communicate and present findings and recommendations to stakeholders
• Act as a champion for data quality and usability across the organization
• Ensure availability, quality, searchability of the data and documentation
• Write, clean, and maintain SQL/Python code that is easy to understand, simple to troubleshoot and are highly performing with proper test implementation
• Automate the testing of our data models and monitor their quality
• Work with the team to continuously improve our data pipeline
• Adopt engineering best practices, such as version control and design patterns and teach others to adopt these best practices