Key skills
AWSCloudDjangoDockerETLFlaskJavaKafkaKerasKubernetesLinuxMySQLOraclePandasPythonRDBMSScalaScikit-LearnSparkSpringSQLTensorflowMachine LearningTensorFlowscikit-learnAnalyticsDatabricksSAPCommunicationPresentation Skills
About this role
Role Overview
- Design and create environments for data scientists to build models and manipulate data
- Work within customer systems to extract data and place it within an analytical environment
- Learn and understand customer technology environments and systems
- Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop
- Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models
- Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle
- Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment
- Ensure the quality of the delivered product
Requirements
- At least 4 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer
- 4-year Bachelor's degree in Computer Engineering or a related field
- Experience deploying data science models in a production setting.
- Expertise in Python, Scala, Java, or another modern programming language
- The ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets
- Strong working knowledge of SQL and the ability to write, debug, and optimize distributed SQL queries
- Experience working with Data Science/Machine Learning software and libraries such as h2o, TensorFlow, Keras, scikit-learn, etc.
- Experience with Docker, Kubernetes, or some other containerization technology
- Familiarity with multiple data source systems (e.g. JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP)
- Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), storage systems (e.g., AWS, Databricks, Cloudera)
- Production experience in core data technologies (e.g. Spark, Pandas)
- Development of APIs and web server applications (e.g. Flask, Django, Spring)
- Complete software development lifecycle experience including design, documentation, and analytical abilities; ability to translate business requirements and use cases into a solution, including ingestion of many data sources, ETL processing, data access, and consumption, as well as custom analytics
- Excellent communication and presentation skills; previous experience working with internal or external customers.
Tech Stack
- AWS
- Cloud
- Django
- Docker
- ETL
- Flask
- Java
- Kafka
- Keras
- Kubernetes
- Linux
- MySQL
- Oracle
- Pandas
- Python
- RDBMS
- Scala
- Scikit-Learn
- Spark
- Spring
- SQL
- Tensorflow
Benefits
- Remote-First Work Environment
- Casual, award-winning small-business work environment
- Collaborative culture that prizes autonomy, creativity, and transparency
- Competitive comp, excellent benefits, generous PTO plus 10 Holidays (and other cool perks)
- Accelerated learning and professional development through advanced training and certifications