Data Scientist, Sensors

Lyten•San Jose, CA

6d•$155,200 - $232,800•Onsite

About The Position

We are looking for a Data Scientist to join our Sensor team, contributing to sensor behavior understanding and calibration modeling as well as methane detection pipeline and product capabilities. This role focuses on extracting reliable signals from noisy sensor and atmospheric data, with a path from prototype to production deployment.

Requirements

Doctorate degree in sensor analytics, atmospheric science, or applied data science ...OR Master's degree in sensor analytics, atmospheric science, or applied data science AND 3+ years of experience OR Bachelor's degree in sensor analytics, atmospheric science, or applied data science AND 5+ years of experience
Strong foundation in statistical modeling, including Bayesian methods and uncertainty quantification
Experience working with time-series sensor data in real-world conditions, including noise, drift, and missing data
Hands-on experience with sensor calibration and validation (raw outputs, calibration models, correction techniques)
Ability to interpret sensor data through underlying physical and chemical principles, connecting observed patterns to sensor behavior, atmospheric processes, and measurement limitations, and to move beyond descriptive analytics toward mechanistic understanding and hypotheses
Proficiency in Python and SQL, with experience in data science libraries and production-oriented workflows (e.g., cloud platforms and workflow orchestration tools)
Ability to collaborate across hardware, data science, and product teams
Strong communication skills and ability to operate within a technically guided team environment

Nice To Haves

Advanced degree (MS/PhD) in atmospheric science, environmental sensing, or a relevant field, or equivalent experience, preferred

Responsibilities

Contribute to data processing and modeling pipelines for sensor calibration and methane detection, from prototype through production
Apply statistical methods (including Bayesian inference) to quantify uncertainty, detection confidence, and limits of detection
Analyze and model raw sensor and atmospheric data to build calibration, correction, and detection models, distinguishing true signal from environmental variability and instrument artifacts
Collaborate with hardware scientists and engineers to diagnose sensor behavior and improve performance
Contribute to analytical standards, validation frameworks, and performance evaluation methodologies