|May 14, 2023
|Santa Clara, United States of America
Job Duties and Responsibilities
Providing solutions for the deployment, execution, validation, monitoring, and improvement of
data science solutions.
Creating high performance and scalable Machine Learning systems
Building reusable production data pipelines for machine learning models
Collaborating with Data Engineers and Data Scientist to build data and model pipelines and help
in running machine learning tests and experiments
Helping to manage the infrastructure and data pipelines needed to bring an Client solution to
Troubleshooting production machine learning model issues, including recommendations for
retrain, revalidate, and improvements
We are seeking people who are passionate about:
Working with data in all forms (structured, unstructured, images, etc) and using it creatively to
help solve problems
Collaborating with data scientists and data engineers to make a big impact
Staying in-tune with the big data community and encouraging the organization to utilize cutting
edge technologies to innovate and invent solutions.
Skills - Experience and Requirements
You would be considered a great fit for this role if you have the following:
Bachelor's degree in computer science engineering, Data Science, or a related technical degree.
In-depth knowledge of machine learning data structures and modelling, software architecture,
libraries and frameworks
Software development experience using Java and related Client libraries including strong
understanding of software engineering principles.
Effective oral and written communication skills to collaborate with others.
Sound problem-solving skills to refine prototypes and troubleshoot performance issues.
These qualifications would make you stand out among other applicants:
Experience deploying and managing containerized applications, preferably using AWS
Experience with big data tools and processing technologies such as Apache Spark, Apache Flink,
and cloud platforms like AWS
Experience in Java or Python.
Familiarity with data-oriented workflow orchestration frameworks such as Kubeflow, Airflow