About this role
About the Role The AI/ML Advanced Data Analytics Engineer will be part of a core solution development team working with domain experts, application developers, controls engineers, data engineers and data scientists. Their primary responsibility will be to design and develop solutions to automate data access, data visualization and develop machine learning models. These systems & solutions will span both cloud and on-premises environments and will require close collaboration with many technical teams to ensure success. What You'll Do
- Develop and implement AI/ML data science methodologies and AI products that align with business objectives.
- Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence to solve manufacturing and business problems.
- Collaborate with data scientists, process engineers, and controls engineers to understand and automate data operations like transformation and normalization.
- Design monitoring systems to ensure the models perform well.
- Generate documentation on data analytics pipelines and processes.
- Participate in advanced data analytics COPs (communities of practice) to spread knowledge and drive governance and reusability of data analytics topics.
- Leverage tools such as Databricks and AWS Datasync to manage data, build models, and deploy solutions.
- Stay abreast of the latest data science and AI/ML trends and technologies, identifying opportunities for innovation and improvement.
- Travel domestically up to 25% and work across cloud and on-prem environments as needed.
- The role will require collaboration across multiple technical teams to ensure successful delivery. What We're Looking For
- Bachelor's degree in Computer Science, Statistics, Engineering or related scientific field (preferred).
- 2-4 years of relevant technical experience in a manufacturing environment.
- Strong understanding of machine learning algorithms, data analysis, and statistical modeling techniques.
- Proficiency in Python and .NET/C#.
- Ability to communicate effectively with users and cross-functional teams to collect requirements and describe data modeling decisions.
- Familiarity with big data concepts, ETL/ELT pipelines for data warehousing, and on-premise and cloud data lake environments.
- Familiarity with Databricks and AWS platform services.
- Cultural bias towards continual learning, sharing best practices, and elevating less experienced colleagues.
- Domestic travel up to 25%. Nice to Have
- Additional familiarity with data governance and governance best practices.
- Experience with data visualization and dashboarding approaches.
- Experience developing and deploying ML models in production. Compensation & Benefits
- Salary not disclosed.
- Travel up to 25% (domestic).
- Typical schedule: around 40 hours/week, with potential for more depending on project requirements.