Research Software Python Data Engineer

at  Waters Corporation

Overview:
Come help us research and develop self-diagnosing, self-healing instruments!
The Waters Global Research department is exploring state-of-the-art capabilities that will stretch your creative talents. Our aim is to enhance our customers’ user experience by building more intelligent systems. Our analytical chemistry instruments have a direct impact on laboratory testing, drug discovery and development and food safety, and we strive to enhance our software offerings, making them more intuitive and easier to use.
The current work focuses on training machine learning and other statistical models that perform root error diagnosis using raw signals time series data coming from our instruments. Other projects include automating steps that users currently do manually, such as interpreting raw data results, adjusting anomalous data to clean it up, and optimizing the procedures that the instruments run on.
The role of this Data engineer will be to develop data pipelines for specialty instrument data, support the development of classification and prediction models, create and maintain dashboards to monitor data health, and set up and maintain services in AWS to deploy models, and collect results. These pipelines will be part of foundational emerging data infrastructure for the company. We seek someone with a growth mindset who is self-motivated, a problem solver, and someone energized by working at the nexus of leading-edge software and hardware development.
Responsibilities:

Responsibilities:

• Build python data pipelines that can handle data frames and matrices, ingest, transform, and store data using pythonic code practices.
• Create and maintain dashboards to monitor data health.
• Maintain codebase: use OOP and/or FP best practices, write unit tests, etc.
• Use Docker to containerize models and deploy them to AWS
• Perform some maintenance of AWS services, such as S3, Lambda, and EC2.
• Work with Machine Learning engineers to evaluate data and models, and present results to stakeholders in a manner understandable to non-data scientists.

As a team member you will:

• Participate in all team meetings and ceremonies in direct collaboration with other sites, provide input and feedback, take ownership on identified improvements
• Actively participate in learning and sharing activities either during informal or formal training and demos
• Demonstrate continuous technical improvement

Qualifications:

Required Qualifications:

• Bachelor’s in computer science or related field and at least 3 year relevant work experience, or equivalent. Position level commensurate with experience.
• Proficient in Python (including numpy and pandas libraries).
• Experience writing object-oriented
• Some experience in AWS services such as S3, EC2, Lambda, and IAM.
• Some experience using Docker to containerize and deploy code in AWS.
• Comfortable with Git version control, as well as BASH or command prompt.
• Comfortable discovering and driving new capabilities, solutions, and data best practices from blogs, white papers, and other technical documentation.
• Able to communicate results using meaningful metrics and visualizations to managers and stakeholders and receive feedback.
• You are fluent in English - speaking, reading, writing - Advanced Level

In return;
You will receive competitive compensation, great benefits and continuous professional development.
We’re actively building diverse teams and welcome applications from everyone. But simply having a diverse workforce is not enough. We aim to build an inclusive environment, where everyone can contribute their best work and develop to their full potential. We celebrate our differences and recognise the importance of teams reflecting the communities they serve.
We can make reasonable adjustments to our interview process according to your needs.
Company Description:
Waters Corporation (NYSE: WAT), the world’s leading specialty measurement company, has pioneered chromatography, mass spectrometry and thermal analysis innovations serving the life, materials, and food sciences for over 60 years. With approximately 8,000 employees worldwide, Waters operates directly in 35 countries, including 15 manufacturing facilities, with products available in more than 100 countries. Our team focuses on creating business advantages for laboratory-dependent organizations to enable significant advancement in healthcare delivery, environmental management, food safety, and water quality.
Working at Waters enables our employees to unlock the potential of their careers. Our global team is driven by purpose. We strive to be better, learn and improve every day in everything we do. We’re the problem solvers and innovators that aren’t afraid to take risks to transform the world of human health and well-being. We’re all in it together delivering benefit as one to provide the insights needed today in order to solve the challenges of tomorrow.
Diversity and inclusion are fundamental to our core values at Waters Corporation. It is our responsibility to actively implement programs and practices to drive inclusive behavior and increase diversity across the organization. We are united by diversity and thrive on it for the benefit of our employees, our products, our customers and our community. Waters is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or protected Veteran status

• Build python data pipelines that can handle data frames and matrices, ingest, transform, and store data using pythonic code practices.
• Create and maintain dashboards to monitor data health.
• Maintain codebase: use OOP and/or FP best practices, write unit tests, etc.
• Use Docker to containerize models and deploy them to AWS
• Perform some maintenance of AWS services, such as S3, Lambda, and EC2.
• Work with Machine Learning engineers to evaluate data and models, and present results to stakeholders in a manner understandable to non-data scientists