Job Description
Identify new opportunities to improve business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset.
Work with data scientists and analysts to create and deploy new product features on the ecommerce website, in-store portals and mobile app
Implement end-to-end solutions across the full breadth of ML model development lifecycle.
The specific role includes working hand in hand with the scientists from the point of data exploration for model development to the point of building features,
You will have an opportunity to work on both batch and real time models. The role also involves operational support.
Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation
Write efficient and scalable software to ship products in an iterative, continual-release environment
Contribute to and promote good software engineering practices across the team and build cloud native software for ML pipelines
Contribute to and re-use community best practices
Example Projects
Customer Segmentation
Automated text summarization and clustering
Next-Best offer prediction
Design Micro assortments for Next-Gen stores
Anomaly detection and Root Cause Analysis
Unified consumer profile with probabilistic record linkage
Visual search for similar and complementary products
About You
University or advanced degree in engineering, computer science, mathematics, or a related field
7+ years' experience developing and deploying machine learning systems into production, and independent contributor.
Comfortable with Python ecosystem, vscode, jupyternotebooks.
Experience working with big data tools: Hadoop, Spark, Kafka, etc.
Experience with at least one cloud provider solution (AWS, Google Cloud Platform) and understanding of serverless code development (Google Cloud Platform preferred)
Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc. (Python preferred)
CI/CD expert. And can work on GitHub actions, harness, Jenkins
Can work with Google Big Query, or similar warehouse.
Work on Kubeflow pipelines independently and propose standards.
Knowledge of Feature Engineering, Feature Store, and audit capabilities.
Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation
Working experience with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, TFX...
Relevant working experience with Docker and Kubernetes is a big plus
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 80183517
- Position Id: e3fb0a4d9cf3ce354e6183c77fa24ddb
- Posted 3 hours ago
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