R&D Engineer – Data-Driven Wearable Tech

Requirements

• Educational Background: BS or higher in Electrical Engineering, Computer Science, Mathematics, Statistics, or a related field
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• Experience:
  
  7+ years of experience
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• Problem-Solving Expertise:
  Strong ability to apply data analytics to solve complex engineering challenges and propose creative, data-driven solutions
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• Statistical
  
  Skills:
  
  Proficiency in applying advanced statistical techniques, particularly in time-series data analysis, using Python or similar tools (e.g., Minitab)
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• Hands-On Engineering:
  Experience with hardware prototyping and debugging, utilizing lab equipment such as oscilloscopes, multimeters, and impedance analyzers
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• Circuit Design Expertise:
  Solid understanding of circuit design principles
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• Software & Code Management:
  Experience maintaining and improving live code for hardware systems, including logging, monitoring, and debugging in development environments
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• The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office
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• Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply

• We are seeking an exceptional multi-disciplined Staff R&D engineer. As a key member of our Electrical Engineering team within the hardware organization, you will be working at the intersection of data and design, using advanced analytics to influence product strategy and technical decision-making
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• Our hardware team thrives on rapid prototyping and data-driven insights, so hands-on experimentation and a keen ability to extract meaningful conclusions from complex datasets are essential. If you're passionate about using data to shape the future of wearable technology, we want you on our team
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• Advanced Development Projects:
  Spearhead early-stage design and development of biometric wearable devices, using data analytics to guide decision-making and determine success criteria
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• Data-Driven System Validation:
  Design and execute system-level validation protocols, utilizing engineering fundamentals combined with statistical methods to ensure optimal performance in real-world scenarios
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• Advanced Data Processing & Statistical Techniques:
  Use Python and other statistical tools to process large data sets, conduct feasibility testing, and inform engineering design decisions
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• Circuit Design & Simulation:
  Apply an understanding of circuit design principles and tools like SPICE for designing and validating hardware circuits in wearable technology systems
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• Prototyping & Functional Verification:
  Test and validate hardware prototypes for in-depth functional testing, utilizing a variety of lab equipment for debugging and analysis
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• Test Design & Failure Investigation:
  Define system requirements and develop test strategies, analyzing test data to ensure validation. Lead failure investigations using data to identify root causes and improve designs
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• Cross-Functional Collaboration:
  
  Partner with teams across hardware, software, data science, and product to shape development strategies, ensuring data insights drive both technical and strategic decisions
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• Stakeholder Engagement:
  Collaborate with external partners, suppliers, and contract manufacturers to ensure successful development and scale-up of products