We are looking for a Signal Processing and Data Analysis Scientist to be a part of the team that designs, implements, tests algorithms and conducts data analysis for Sano’s novel sensor platform focused on continuous body chemistry monitoring.
What you'll be doing:
Mathematically model complex, stochastic physical phenomena associated with a wearable electro-chemical bio-sensor which is used for real-time, physiological analyte estimation.
Develop new and improve existing signal processing algorithms for bio-sensor anomaly detection and correction.
Implement new and existing proof-of-concept algorithms (for basic analyte tracking, subsequent real-time inference of user state, identification and estimation of meaningful, long and short-term metrics associated with user health) in production-quality, cloud-based software.
Contribute to the design of experiments intended to isolate error sources. Statistically analyze the impact of errors on performance.
What you'll bring:
- Experience in statistical modeling, time-series analysis, and signal processing (i.e., digital filtering, spectral analysis, probability, multi-dimensional random processes).
- Expertise using variety of classical and modern methods of statistical inference and learning (i.e., regression, point and interval estimation, hypothesis testing, classification, Markov-modeling, clustering) for solving practical problems.
- Expertise in Python (pandas, numpy, scipy, matplotlib) and best-practices methodologies (i.e., revision control, test-based development).
- Excellent written and verbal communication skills.
- Systematic thinker who displays a blend of theoretical rigor and practical, deadline driven task completion.
- Masters or Ph.D. in Engineering, Physics, Computational biology, Mathematics, Statistics or related field required with 8+ years of direct, relevant industry experience.