Senior Principal Machine Learning Engineer

Key Skills

Aartificial intelligence, API, AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Dataiku, DevOps, Effective communication skills, English Proficiency, GitLab, Keras software library, Kubeflow, Machine learning techniques, MLOps tools, NoSQL, Python Programming, PyTorch, R Programming, Scikit-learn, Spark Core, SQL, Statistics, TensorFlow, Unix Operating system

Job Description

• Organization: Chief Data and Analytics Office
• Team: Enterprise Data Science Center of Excellence
• Job Field: Advanced Analytics / Machine Learning Engineer
• Job Level: Individual Contributor (8+ years experience)

Description:

The Enterprise Data Science Center of Excellence (CoE) team, within the Chief Data and Analytics Office, works beyond enabling the business users with tools and technologies that help them with their information discovery and analysis. While serving mainly a technical role, this CoE is also responsible for guiding the broader organization with competency development, Human Resources strategy to attract and retain top data science talent, and consult with senior leadership on corporate-priority projects. We know a community of similar practitioners is important to you, this is an opportunity for you to be the integrator across the existing data science community, then mobilize this community to emerge your vision of Enterprise Data Science. 

This Machine Learning Engineer will focus on helping our internal business partners accelerate the transformation of advanced analytics prototypes into operationalized work. You will work closely with the data scientists on your team, as well as data scientists embedded into the business to provide guidance on refactoring advanced analytics solutions and fostering their movement through the production pipeline.

Our ideal candidate is a problem solver who enjoys understanding in depth business challenges and defining/implementing innovative solutions. This team is new and growing, and they would be comfortable with ambiguity and self-identifying areas of opportunity for improvement. Expect exposure to a wide range of enterprise organizations, such as Finance, Supply Chain, Human Resources, Global Sales and Marketing, Manufacturing and Operations, IT, etc. 

We’re not starting from zero….we're actively developing advanced analytics use cases and putting them into production. We have additional use cases waiting for you as well as the funding and support from senior leadership to develop these solutions and put them into production. The only thing missing is you!

Job Responsibilities:

• Be an acting member of the Enterprise Data Science Center of Excellence, and our ongoing enterprise Digital Transformation Initiative.
• Develop ML Ops vision and strategy for the enterprise, considering the short and long term needs and constraints.
• Work closely with your fellow Data Science CoE data scientists and embedded business data scientists to translate prototypes of use cases into production systems. Build ML pipelines and workflows to efficiently manage the ML lifecycle.
• Provide guidance to our data engineering team on data storage and retrieval best practices from ML Ops perspective, based on specific use cases.
• Mentor/Coach more junior data scientists and machine learning engineers, occasionally leading trainings on special topics.
• Consult with our various IT architecture boards to provide recommendations on the right technical approach for our business partners data science use cases and provide guidance which will allow them to formalize it in a technical proposal, including advising on architecture, best practices and refactoring existing solutions to optimize efficiency/cost.

Skills & Qualifications

• Minimum, B.S. in a STEM related degree (Engineering, Computer Science, Physics, etc.) or Econometrics, and relevant follow-up graduate studies, such as: Master of Computational Data Science, Master of Science in Machine Learning, Master of Science in Cognitive Science, Master of Science in Artificial Intelligence, Master of Science in Computer Science with AI Specialization, or other related quantitative discipline with a strong foundation in statistics. 
• 8+ years of experience in advanced analytics space with hands-on experience in: model development (Python, R, Spark, TensorFlow, Keras, PyTorch, Scikit-Learn, Dataiku, etc.), infrastructure and orchestration (AWS, Azure, Dataiku, SQL/NoSQL databases, Bash, Docker, Kubernetes (EKS, AKS), Azure Resource Manager, Terraform, etc.), CI/CD/MLOps (Kubeflow, Gitlab, Azure DevOps, etc.) and integrating API endpoints for serving machine learning models.
• Strong coding proficiency is a must.
• Expertise in building production level ML solution, architecture, and pipelines both in batch, and real-time.
• You have built large-scale production ML models and systems with a track record of innovation and having taken large machine learning-based products, and features, from conception to successfully delivering value to customers in production.
• Proficiency with Unix-based operating systems (i.e., command line interface, SSH, networking, resource utilization, etc).
• Experience with Enterprise-scale cloud and hybrid infrastructures, architecture designs, and technology management.
• Passion for creative problem solving, data science, machine learning operations and culture building.
• Know how to learn! Know how to Mentor!
• Must be fluent in English

NXP is an Equal Opportunity/Affirmative Action Employer regardless of age, color, national origin, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, marital status, status as a disabled veteran and/or veteran of the Vietnam Era or any other characteristic protected by federal, state or local law. In addition, NXP will provide reasonable accommodations for otherwise qualified disabled individuals.