Grafana Labs · 1 day ago

Staff Data Scientist (Remote, US)

United States (Remote)

Full-time

Remote

Senior Level, Lead/Staff

$196K/yr - $246K/yr

5+ years exp

Maximize your interview chances

AnalyticsCloud Data Services

Comp. & Benefits

Insider Connection @Grafana Labs

Discover valuable connections within the company who might provide insights and potential referrals.
Get 3x more responses when you reach out via email instead of LinkedIn.

Responsibilities

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

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

ML modelsTime series dataPythonSQLCloud data warehousesBayesian statisticsGrafana visualizationObservability

Required

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