Data Science Engineer

Key Skills

Java Programming, Design, Python Programming, SQL, ETL frameworks

Job Description

Our vision is to transform how the world uses information to enrich life for all.

Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.

JR31899 Data Science Engineer

Responsibilities include, but not limited to:

Design and Architect System Integration Solutions

• Design and architect system solutions for multi-pass pick/die level cherry pick flows
• Work with cross functional teams to define and drive data infrastructure, system integration and master data roadmap and architecture
• Design, architect, and develop a scalable and supportable data pipeline which includes data warehousing, data integration and ETL processing
• Document artifacts related to software development, design, test cases, user training, release.
• Lead/Participate in custom data integration projects with internal data systems and/or external sub con manufacturing and logistics systems
• Collaborate with IT architectures/teams to design and prioritize infrastructure delivery with processes, deliverables aligned to roadmap.

Enforce Operational Data Quality

• Establish development best practices and tools to support data integrity, cleanup, and validation across all relevant data sources
• Enable continuous data quality improvements to minimize support requirements
• Implement central logging and monitoring systems with logic for usage reports and audits
• Collaborate with IT to ensure effective incident, problem and change management processes

Design and Develop Data Analytics

• Develop expertise in data mining and analytic methods
• Solve complex optimization problems using linear and nonlinear optimization techniques
• Develop and generalize data science solutions in a production data pipeline using statistical and machine learning techniques
• Determine statistical validity and significance (pick out signals from noise)
• Apply statistical methods to detect anomalies, trends and shifts

Develop Modern Visualization Solutions

• Automate business reporting needs and organize into user friendly dashboards in multiple platforms (desktop/web/mobile)
• Facilitate interactive design sessions with customers
• Use experimentation methods and design tests to optimize key user experiences