Job Description At Boeing, we innovate and collaborate to make the world a better place. 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.

You will be responsible for extracting valuable insights from complex datasets, identifying patterns, trends, and anomalies, and developing predictive models to support strategic decision-making across the organization. You will collaborate closely with cross-functional teams to design and implement data-driven solutions that address business challenges and drive continuous improvement • Analyze large-scale time-series and tabular datasets to extract meaningful insights and identify actionable opportunities. • Develop and implement advanced statistical models, machine learning algorithms, and forecasting techniques to predict future trends and outcomes. • Design and execute experiments to evaluate model performance, validate hypotheses, and optimize algorithms for accuracy and efficiency. • Collaborate with stakeholders to define business requirements, prioritize analytical projects, and translate findings into actionable recommendations. • Build and maintain robust data pipelines and automated workflows to streamline data collection, preprocessing, and analysis processes. • Communicate complex technical concepts and analytical findings to non-technical stakeholders through clear and concise presentations, reports, and visualizations. • Stay abreast of the latest developments in data science, machine learning, and statistical techniques, and evaluate their potential impact on business objectives. • Works with MLOps Engineers to derive crucial features from the data processed in ML models • Works with a Project Lead and a Program Management Specialist to decompose and partition system requirements to software requirements • Works as a member of the international team with the members in one or more countries where English is the common language

• Bachelor's or advanced degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. • Proven experience (3~ years) in data science, with a focus on time-series and tabular data analytics. • Proficiency in programming languages such as Python, and experience with libraries/frameworks such as pandas, NumPy, scikit-learn, TensorFlow, or PyTorch. • Strong understanding of statistical concepts, hypothesis testing, regression analysis, time-series modeling, and machine learning algorithms. • Experience with data visualization tools such as Matplotlib, Seaborn, Plotly, or Tableau. • Solid understanding of database systems, SQL, and data manipulation techniques for extracting, transforming, and loading (ETL) data. • Applicants must have the enthusiasm to learn new technologies and expand related skillsets • This role requires creativity, critical thinking, and troubleshooting skills, at least, for Exploratory Data Analysis (EDA) work • Strong verbal and written communication skills in English and Korean • The ideal candidate is a self-starter and someone who works well within a team • Highly motivated "roll up your sleeves" attitude with a strong drive for success

• Master’s or Ph.D. degree in Engineering, Computer Science, or Math etc. • Good to have at least an intermediate level of knowledge in aircraft manufacturing or flight operation • Nice to have winning experience in machine learning or data science competition challenges • Nice to have your git repository shared with well-organized projects inside • Preferably experiences in Data processing and database programming skills, such as SQL or NoSQL • Experience in the whole lifecycle of a system development project ranging from developing a new service concept to launching as a service entry into this classification is limited to candidates approved by the enterprise-wide Data Science & Analytics Skills Team. Applicable and appropriate educational/certification credentials from an accredited institution and/or equivalent experience is required.

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