Applied Data Scientist (Python coding, MLOps, AWS, ETL)

Position: Sr. Applied Data Scientist

EDUCATION/EXPERIENCE REQUIREMENTS

• Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or related technical field.
  6+ years of hands-on experience in applied data science, machine learning, or AI system development.
  3+ years of experience building and deploying ML/AI models integrated with production software (C++, Python, Go or related language).
  Proven experience working with AWS Cloud for model training, data pipelines, and deployment (SageMaker, Lambda, Glue, ECS/EKS, Redshift, S3).
  Practical experience developing visualizations and dashboards using Amazon QuickSight or similar BI tools.
  Familiarity with containerization (Docker) and orchestration (ECS/EKS) for scalable, fault-tolerant deployments.
  Understanding of MLOps principles, CI/CD pipelines, and model monitoring practices.
  Data Science or AWS Cloud certification preferred.

JOB SUMMARY

• Lead the design and development of applied ML and AI solutions that directly support Panasonic s inflight system platforms.
  Build and deploy scalable models that integrate with core applications written in C++, Python, and Go, supporting real-time decision-making across connectivity, content, and operational services.
  Develop and operationalize predictive, prescriptive, and optimization models using AWS services such as SageMaker, Lambda, Glue, ECS/EKS, Redshift, and S3.
  Use Amazon QuickSight and similar tools to visualize model outcomes, performance trends, and business insights.
  Lead applied research and experimentation with Panasonic s operational data to design lightweight Business Growth Models (BGMs) and other analytical frameworks & strategies.
  Collaborate closely with data science, data engineering, and product teams to bring scientific models into production, ensuring stability, scalability, and measurable business impact.
  Explore and apply emerging ML and optimization methods, containerization (Docker, ECS, EKS), and MLOps best practices to improve model reliability and automation.
  Stay current with developments in applied AI and embedded systems, evaluating opportunities to strengthen Panasonic s data-driven aviation ecosystem.

KNOWLEDGE/SKILL REQUIREMENTS

• Strong software-engineering foundation; proficiency in Python and at least one compiled language (C++, Python, or Go).
• Ability to integrate ML models directly into core software systems using APIs, microservices, or embedded components.
• Deep understanding of machine learning, statistical modeling, and optimization techniques for operational use cases.
• Experience designing and automating end-to-end ML pipelines using AWS and modern MLOps tooling.
• Knowledge of ETL, data wrangling, and feature engineering for large-scale structured and unstructured data.
• Experience developing containerized model services and working with orchestration frameworks for production scaling.
• Proficiency with QuickSight or similar tools to visualize data trends, model diagnostics, and KPI tracking.
• Familiarity with data governance, quality, and metadata management practices.
• Strong analytical and communication skills; able to convey technical concepts clearly to engineering and business teams.
• Experience mentoring peers and driving adoption of modern data science and software practices.

MAJOR RESPONSIBILITIES Description

• Design, develop, and deploy ML and AI models integrated with Panasonic s core software stack (C++, Java, Go).
  Build and maintain containerized, production-ready pipelines and inference services using AWS Cloud.
  Collaborate with software and data engineering teams to design APIs and integration patterns for embedding ML capabilities within inflight and airside applications.
  Lead applied research on operational data to develop lightweight Business Growth Models (BGMs) that optimize inventory and ad placement strategies.
  Apply advanced ML and statistical methods to improve operational efficiency and passenger experience.
  Establish and maintain MLOps best practices for continuous integration, model retraining, and monitoring.
  Partner with product managers to define measurable success criteria and ensure deployed models deliver business value.
  Mentor junior scientists and engineers on applied ML development, testing, and deployment.
  Stay informed on emerging ML/AI frameworks and identify opportunities to enhance Panasonic s data-driven capabilities.

MAJOR RESPONSIBILITIES % of Time

• 45 % Model & Software Development
  35 % Deployment & MLOps
  20 % Research, Collaboration & Mentorship