5+ years experience in a manufacturing environment, with functional knowledge across multiple departments and domains (transactions, finance, process, product) is required
Demonstrated ability to transform data with a programming language
Demonstrated project management skills
Experience with an engineering data tool: SAS, Minitab, JMP, Advanced MS Excel
Familiarity with Six Sigma methodology or intermediate statistics
Microsoft Office skills (Word, Excel, Power Point, Visio)
Strong written and verbal communication skills
Demonstrated ability to work effectively in both self-directed and collaborative matrixed team environments
This position may require travel up to 25%
Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.
This position entails access to export controlled items and employment offers are conditioned upon an applicants ability to lawfully obtain access to such items.
Preferred Qualifications
Demonstrated ability using python for data transformation, analysis, and visualization
As an Analyst Engineer at the Howmet Research Center, you will join a team of other Analyst Engineers and Data Scientists. This team is part of a multidisciplinary R&D group responsible for advancing the state-of-the-art in aerospace manufacturing at our casting, alloy, core and rings manufacturing facilities. You are a great fit for this team if you have the innate curiosity necessary to learn new processes, meet new people, and are persistently detail-oriented with a quality mindset. If you enjoy matching complex physical processes to corresponding data across multiple data sources, and transforming data into reliable, documented datasets for use in machine learning, reporting, and visualization tools, we would love to talk with you! Engineering data super-users looking to transition to a data field are welcome.
As an Analyst Engineer, a typical project requires you to:
Initiate working with manufacturing plant personnel to understand the detailed manufacturing process and data collection user interfaces
Initiate working with corporate and local IT to understand the data storage and sources that correlate to plant processes, and the most efficient way to query them
Query multiple data sources and join the data together to accurately represent the process
Explore the data to identify edge cases, data cleaning requirements, and bias - using statistics as necessary
Down-select, augment, and clean the dataset
Iterate the above steps to generate a representative and reliable dataset, checking your own work and being able to explain it to others at multiple levels of the organization
Collaborate with Data Scientists to understand model input requirements
Transform the data to meet machine learning model input requirements
Provide post-modeling functions to translate model results to process-relatable values
Actively participate in leveling up proof-of-concept code to productionized scripts
Document projects in Power Point and reusable code, storing work in a revision-controlled system