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Boeing · 2 days ago

Senior Data Scientist

USA - Dallas, TX

Full-time

Hybrid

Senior Level

$173K/yr - $235K/yr

5+ years exp

The Boeing Company is currently seeking a Senior Data Scientist to join the team in Dallas, TX. This role involves developing and building next generation Artificial Intelligence and Machine Learning products and services, while working with cross-functional teams to deploy analytics solutions that meet business goals.

AerospaceIndustrial

No H1B

Responsibilities

Evaluate business objectives, determine stakeholder needs, and identify requirements

Choose best fit methods, define algorithms, validate, and deploy models to achieve business results

Perform necessary data preparation and enhancements to models

Interface directly with senior business leaders and stakeholders to architect and lead the development of advanced analytics solutions supporting Aircraft operations, Engineering, Manufacturing, Quality, and Safety

Work with data scientist leads across domains to ensure functional excellence in this technical field and will provide mentoring, technical leadership, and training to the data scientist community to accelerate and develop this talent base

Provide thought leadership for machine learning and advanced analytics methods at Boeing and externally

Engage with stakeholders across the business to identify applications across the enterprise, beyond the initial use case, and lead the implementation of these applications to capture business value

Evaluate technologies, assist in identifying capabilities gaps, develop and teach best practices and technology standards, and work alongside the rest of the global data scientists and technical leads in Analytics organization

Ensure analytics solutions meet the business requirements and are implemented in a timely manner to validate business value

Work effectively with cross-functional teams in the analysis of highly complex data sets using advanced analytics techniques such as machine learning, advanced statistical analysis, visual analysis, text analysis, mathematical optimization, and simulation

Identify modeling attributes and parameters

Follow best practices and standard processes for model validation and refinement as per business requirements

Assess performance of models and analysis and confirm that business objectives were met

Work with business unit stakeholders to develop and sustain analytics solutions

Qualification

Machine LearningNatural Language ProcessingDeep LearningGenerative AICloud ComputingStatistical SoftwareProgramming PythonProgramming JavaAdvanced StatisticsAgile DevelopmentSupply Chain ExperienceTeam Leadership

Required

5+ years of experience working with projects, teams, and/or leaders in the analytics and/or data science space

5+ years of experience researching and applying large language and generative AI models

5+ years of experience with various machine learning methods such as regression, clustering, classification, decision trees, natural language processing, ensemble methods, support vector machines (SVM), deep learning, reinforcement learning, etc

5+ years of experience in designing and implementing Natural Language Processing (NLP) models using machine learning techniques including neural networks, deep learning and transformer architectures

Preferred

Bachelor's degree or equivalent work or military experience

Master's degree in data science or equivalent

Experience in supply chain domain

Experience in agile and product-oriented development

Experience in building ML models and bring in to full production use within the industry

Experience training machine learning models in a cloud computing environment such as: Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc

Experience programming in one or more of the following: C, C++, Java, Python, Scala

Experience with statistical software (e.g., R, MATLAB) and database languages (e.g., SQL)

Experience in advanced statistics (e.g., probability theory and distributions, descriptive statistics, matrix algebra, multivariate statistics, polynomial analysis, parametric and non-parametric methods, etc.)