Job Description:

Client is looking for a Lead Machine Learning Engineer to join our growing cross-functional team. You will work on a variety of complex business problems related to time series forecasting and inventory optimization. You will leverage big data, parallel processing technologies, advanced analytics, machine learning, and deep learning techniques to quantitatively plan product demand, allocate resources, and target the right customers with the best products. Above all, your work will accelerate Client's core mission of serving Athletes*.

What you’ll do

• Lead and work with other engineers and data scientists in an agile environment to apply machine learning methods (e.g. NLP, computer vision, transfer learning) to massive data sets

• Contribute to the design of ML infrastructure needed to enable analytics at scale (e.g. model training/discovery/management/serving capabilities)

• Lead the investigation and implementation of emerging ML technologies/frameworks from both the open source and vendor communities

• Provide technical leadership and support for team members. Role model and document best practices within your team. Drive quality through continuous testing and improvement.

Who you are

• Rock solid engineer and data scientist with 5+ years experience in machine learning, AI and distributed systems development.

• Master’s Degree or PhD in computer science, mathematics, engineering, or other related quantitative field.

• Significant expertise in employing machine learning and deep learning models for time series forecasting and unstructured data processing.

• Demonstrated experience implementing machine learning systems at scale in Python, Java and/or C, C++

• You have a strong mathematical background in statistics and machine learning

• You care about agile software processes, data-driven development, reliability, and responsible experimentation

• You preferably have experience with data processing and storage frameworks like Hadoop or S3, Snowflake, Spark, Flink, Cassandra or Dynamo, Kafka, etc.

• You have experience deploying machine learning models as APIs

• You have experience implementing deep neural networks in Tensorflow/Distributed-TF, PyTorch, Go

• You preferably have machine learning publications, project code, or work on open source to share with us