Data Science Engineer (Associate or Mid-Level)

at  Boeing

At Boeing, we innovate and collaborate to make the world a better place. From the seabed to outer space, you can contribute to work that matters with a company where diversity, equity and inclusion are shared values. We’re committed to fostering an environment for every teammate that’s welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us.
Join an exciting and growing team in performing complex data analysis of aviation data. As modern aircraft produce increasing amounts of data in operations and maintenance, analytics are used to turn that data into insights to improve performance, enable smarter services, and increase productivity. As a member of the Boeing Global Services (BGS) Engineering Analytics team, the candidate will work with a group of data scientists in the Seattle area to analyze data and provide advanced analytics technologies, including development, adaptation, and maturation of analytics capabilities for a wide range of applications across BGS business and engineering work streams.
The BGS Data Sciences team is looking to hire a data scientist with advanced knowledge in fields such as machine learning techniques, advanced statistics, text analytics, and natural language processing. These skills will be used to solve challenging problems centered around engineering and in-service data in operations research, managed services, predictive maintenance, and logistics optimization to support cross-functional teams to determine, define, and deploy data analytics to meet business objectives, investigate and apply best fit methods and algorithms, validate results, and perform necessary data preparation and enhancements to models.

BASIC QUALIFICATIONS (REQUIRED SKILLS/EXPERIENCE):

• 1+ year(s) experience in SQL
• 1+ years of experience with C/C++ coding or java/python script development
• Cloud application development and deployment expertise
• 2+ years of experience with data analytics capabilities and data science processes as an analyst

PREFERRED QUALIFICATIONS (DESIRED SKILLS/EXPERIENCE):

• 3+ years of higher education and/or related work experience. (Higher education includes college, university, technical school, licensing/certification programs, etc.)
• Experience working with services based business or engineering offerings
• Knowledge in developing ML pipelines in cloud based environments
• Experience in an agile software development team
• Experience with cloud computing services such as GCP, Azure or AWS, experience

TYPICAL EDUCATION/ EXPERIENCE:

Associate (Level 2)
Education/experience typically acquired through advanced technical education from an accredited course of study in engineering, computer science, mathematics, physics or chemistry (e.g. Bachelor) and typically 2 or more years’ related work experience or an equivalent combination of technical education and experience (e.g. Master). In the USA, ABET accreditation is the preferred, although not required, accreditation standard.
Associate (Level 3)
Education/experience typically acquired through advanced technical education from an accredited course of study in engineering, computer science, mathematics, physics or chemistry (e.g. Bachelor) and typically 5 or more years’ related work experience or an equivalent combination of technical education and experience (e.g. PhD, Master+3 years’ related work experience). In the USA, ABET accreditation is the preferred, although not required, accreditation standard.

• Modifies and analyzes standardized data sets using analytics techniques such as machine learning, statistical analysis, mathematical optimization and simulation. Identifies modeling attributes and parameters. Applies algorithms to create descriptive, predictive or prescriptive models to achieve analysis and project deliverables
• Iterate and improve the deployment process of complex prognostic alerts to airline operators by optimizing the tech stack, monitor quality of the alerts, and work as intermediary between data scientists and software developers
• Investigates data characteristics, complexity, quality and meaning. Through visualizations and summarizations, defines performance, identifies trends, outliers and drivers
• Prepares data for analysis, e.g. cleansing, conditioning, transforming, handling missing fields, identifying new feature variables and handling multi-variate data
• Supports identifying best-fit data science methodology (statistics, machine learning, optimization, simulation) given the data and business applications
• Performs standard procedures in evaluation and validation of correctness of models and analysis. Supports evaluation and validation of performance of models and analysis
• Supports determination of analysis requirements given business objectives
• Supports determination of decision support needs
• Matches data to requirements and performance metrics to support analysis
• Monitors production and deployment. Prepares decision support visualizations and reports, algorithms, models, dashboards and/or tools