As a Machine Learning Engineer, you are a team player able to work with data scientists, engineers and stakeholders to provide the best personalized results to live event fans around the world.
What the job is:
Build and maintain solutions to match fans to live entertainment events for one of the largest ecommerce platforms globally.
Apply cutting-edge statistical and machine learning algorithms to drive business value through bidding strategies, improved recommendations, anomaly detection, and more!
Shepherd projects from prototype to production deployment: if youre most comfortable with Jupyter notebooks, this may not be the right role for you.
Leverage a test-and-learn approach to appropriately drive value.
Contribute to a culture of continuous improvement, and data driven results.
Our job descriptions evolve with our business need and priorities. In addition to the description above, your role may include additional projects and team support as needed.
What a qualified candidate should possess:
5+ years of experience developing scalable engineering systems. 2+ years developing data science solutions.
Degree in Computer Science, Statistics, Mathematics, Physics, or related field. Advanced degree is a plus.
Python expertise: familiar with virtual environments, dependency management best practices, and unit testing.
Experience with Continuous Integration/Continuous Delivery paradigm and platforms like Travis CI, Circle CI, or Gitlab Pipelines.
Familiarity and experience with containerization (Docker, Kubernetes) and the AWS stack: Data Pipelines, S3, Athena/Redshift, IAM, Secrets Manager, etc.
Experience with feature engineering: analyzing messy data (SQL/pandas) and testing for importance.
Understanding of both classical statistics (hypothesis tests, generalized linear models, ARIMA, Bayesian hierarchical models) and modern Machine Learning approaches (boosted trees, neural networks).
Comfort with ambiguous goals communicated in business terms, not ML terms (meaning: youll have to figure out how to optimize for revenue, not for Mean Squared Error).
Servant leader; mentor; curious and creative problem solver.
Effective visual and verbal communication skills for machine learning concepts and techniques
Well be especially impressed if you have experience with:
Java, Scala, and/or Spark
Sparse and other large-scale numerical methods.
Streaming data, pipeline orchestration, and related technologies, e.g. Kafka, Spark Streaming, Airflow.
Monitoring systems like Grafana/Kibana/Datadog.