Sensor Intelligence Engineer II (Tools & Validation)
Boston, MAFull-TimeMid-levelSoftware Engineering
RESPONSIBILITIES
- Collaborate closely with Data Science and Research teams to enhance user metrics by developing innovative and creative algorithms.
- Partner with WHOOP Labs to plan data collections, define acceptance criteria, and assess algorithm readiness for real-world deployment.
- Validate wearable technology in clinical settings, analyze biomedical data, and prepare comprehensive reports for cross-functional teams.
- Monitor and ensure the proper functioning of algorithms across our diverse user population, addressing issues related to data quality.
- Contribute to ongoing research efforts and explore new features to improve the overall performance of our products.
- Help establish standards and workflows for data analysis, reporting, and dashboard development.
- Participate in software development, debugging, and validation processes to ensure code and results are production-ready.
- Utilize expertise in signal processing and time-series analysis to analyze biosensor systems and optimize their performance.
- Create and implement mathematical models and machine learning algorithms for processing large-scale data and extracting valuable insights.
QUALIFICATIONS:
- Bachelor’s or Master’s degree in applied mathematics, statistics, electrical engineering, biomedical engineering, or a related field.
- A minimum of 2 years of industry or research experience in signal processing and/or machine learning.
- Ability to work with time-series data; familiarity with core signal processing concepts (filters, spectral analysis/FFT, peak detection, windowing).
- Domain exposure to wearable biosignals and real-world noise and artifacts.
- Experience developing back-end tools for data processing and analysis.
- Proficiency in C and/or Python programming languages.
- Solid grounding in statistical inference (e.g., study design, hypothesis testing, regression analysis, bootstrapping), with the ability to choose appropriate methods and articulate trade-offs.
- Familiarity with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, Keras, etc.
- Strong communication skills with a track record of concise, visual storytelling and documentation.
- Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing high-quality reports within tight deadlines.
- Experience with version control systems (e.g., Git), CI/CD pipelines, and cloud computing platforms (e.g., AWS).
- Experience querying data in modern data warehouses (e.g., Snowflake).
