Staff Data Scientist, Grafana Labs

• Extensive experience building production-ready ML models for time series applications
• 5 - 7+ years of experience with Python and familiarity with SQL
• Hands-on experience with cloud data warehouses (e.g., BigQuery, Snowflake, Redshift, etc.)
• Highly motivated self-starter that is keen to make an impact and is unafraid of tackling large, complicated problems
• Excellent communication skills, able to explain technical topics to non-technical audiences, and maintain many of the essential cross-team and cross-functional relationships necessary for the team’s success

Desirable

• We are looking for a Staff Data Scientist to be the founding member of our predictive analytics practice within our Data & Analytics team, with a focus on building capabilities around operationalizing ML models to drive core financial metric and customer consumption forecasts
• In particular, this function will be a key driver in corporate planning and will be heavily relied upon by teams across Finance, Revenue Operations, and our executive team
• Success in this role will require a combination of significant experience in deploying ML models on time series data, coordinating between multiple teams to meet business-driven timelines, and an ability to establish standards and best practices for data science across Grafana Labs
• Examples of projects you’ll work on
• Refine our existing time series forecasts to allow the prediction of per-customer consumption, with an emphasis on ensuring model explainability and a clear representation of model uncertainty
• Partner with Data Engineering to ensure the model infrastructure is in place to serve, monitor, test, and retrain any models put into production
• Identify and build solutions to leverage these forecast models for operational use cases (e.g., customer consumption anomaly detection, alerting, etc.)
• Partner closely with RevOps to both understand and predict customer consumption as a part of our sales planning and territory management
• Collaborate across Product, R&D, and Data Engineering to identify and ingest new sources of data to improve model performance