採用
必須スキル
Machine Learning
Data Scientist (Data Science)
Company:
Boeing Korea LLC:
About Us
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 opportunities for professional growth. Find your future with us.
Boeing Korea Engineering and Technology Center (BKETC) is seeking a Data based in Seoul, South Korea to develop algorithms, models, and systems for our research projects, such as autonomy, aircraft manufacturing, factory digitalization, and pars sales forecasting. Data science work will include extracting, cleaning, analyzing, and visualizing data collected from various business domains at Boeing. This position will report to the Boeing Research and Technology Manager.
Job Responsibilities:
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
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Analyze large-scale time-series and tabular datasets to extract meaningful insights and identify actionable opportunities.
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Develop and implement advanced statistical models, machine learning algorithms, and forecasting techniques to predict future trends and outcomes.
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Design and execute experiments to evaluate model performance, validate hypotheses, and optimize algorithms for accuracy and efficiency.
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Collaborate with stakeholders to define business requirements, prioritize analytical projects, and translate findings into actionable recommendations.
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Build and maintain robust data pipelines and automated workflows to streamline data collection, preprocessing, and analysis processes.
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Communicate complex technical concepts and analytical findings to non-technical stakeholders through clear and concise presentations, reports, and visualizations.
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Stay abreast of the latest developments in data science, machine learning, and statistical techniques, and evaluate their potential impact on business objectives.
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Works with MLOps Engineers to derive crucial features from the data processed in ML models
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Works with a Project Lead and a Program Management Specialist to decompose and partition system requirements to software requirements
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Works as a member of the international team with the members in one or more countries where English is the common language
Basic Qualifications (Required Skills/Experience)
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Bachelor's or advanced degree in Computer Science, Statistics, Mathematics, Engineering, or a related field.
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Proven experience (3~ years) in data science, with a focus on time-series and tabular data analytics.
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Proficiency in programming languages such as Python, and experience with libraries/frameworks such as pandas, Num Py, scikit-learn, Tensor Flow, or Py Torch.
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Strong understanding of statistical concepts, hypothesis testing, regression analysis, time-series modeling, and machine learning algorithms.
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Experience with data visualization tools such as Matplotlib, Seaborn, Plotly, or Tableau.
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Solid understanding of database systems, SQL, and data manipulation techniques for extracting, transforming, and loading (ETL) data.
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Applicants must have the enthusiasm to learn new technologies and expand related skillsets
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This role requires creativity, critical thinking, and troubleshooting skills, at least, for Exploratory Data Analysis (EDA) work
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Strong verbal and written communication skills in English and Korean
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The ideal candidate is a self-starter and someone who works well within a team
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Highly motivated "roll up your sleeves" attitude with a strong drive for success
Preferred Qualifications (Desired Skills/Experience)
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Master’s or Ph.D. degree in Engineering, Computer Science, or Math etc.
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Good to have at least an intermediate level of knowledge in aircraft manufacturing or flight operation
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Nice to have winning experience in machine learning or data science competition challenges
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Nice to have your git repository shared with well-organized projects inside
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Preferably experiences in Data processing and database programming skills, such as SQL or NoSQL
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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.
Language Requirements:
Not Applicable
Education:
Not Applicable
Relocation:
Relocation assistance is not a negotiable benefit for this position.
Security Clearance:
This position does not require a Security Clearance.
Visa Sponsorship:
Employer will not sponsor applicants for employment visa status.
Contingent Upon Award Program:
This position is not contingent upon program award
Shift:
Not a Shift Worker (Korea, Republic of)
総閲覧数
0
応募クリック数
0
模擬応募者数
0
スクラップ
0
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Boeingについて

Boeing
PublicThe Canadian arm of the US aircraft manufacturer Boeing
10,001+
従業員数
Winnipeg
本社所在地
$95B
企業価値
レビュー
3.4
21件のレビュー
ワークライフバランス
2.2
報酬
3.5
企業文化
2.0
キャリア
2.1
経営陣
1.8
25%
友人に勧める
良い点
Good pay and benefits
Job stability compared to other tech companies
Engineers working to improve processes
改善点
Poor management and leadership quality
Terrible work-life balance and high stress
Lack of career advancement opportunities
給与レンジ
27件のデータ
Junior/L3
L2
L3
L4
L5
L6
Principal/L7
Senior/L5
Staff/L6
Junior/L3 · Data Scientist L2
0件のレポート
$106,003
年収総額
基本給
-
ストック
-
ボーナス
-
$90,103
$121,903
面接体験
2件の面接
難易度
2.5
/ 5
期間
14-28週間
内定率
100%
体験
ポジティブ 50%
普通 50%
ネガティブ 0%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Hiring Manager Interview
4
Panel Interview
5
Offer Negotiation
よくある質問
Behavioral/STAR
Product Strategy
Technical Knowledge
Past Experience
Culture Fit
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