Scientific Applications Data Scientist IV

JPL/NASA•Pasadena, CA

2d•$165,360 - $227,864•Onsite

About The Position

The JPL Science Data Systems Section is seeking a talented individual to be part of the Algorithm and Calibration Engineering Technical Group in supporting science data analysis for JPL Earth Science missions. The Scientific Applications Data Scientist will be responsible for the development and implementation of processing algorithms and science data systems for Earth Science spaceborne and airborne missions, while leading the development of AI-based algorithms for efficient data processing across a variety of scientific applications. JPL Science Data Systems Section operates a cloud-based Science Data System (SDS) that ingests satellite telemetry and systematically processes it into calibrated, geolocated, and quality-controlled higher-level science data products. The SDS leverages elastic cloud computing, automated workflows, and robust monitoring to ensure scalable, repeatable, and timely product generation. This architecture enables reliable end-to-end data delivery from mission operations to the global science and applications communities. The JPL Section 342 is actively involved in concept formulations of autonomous on-board SDS for NASA and commercial applications.

Requirements

Bachelor’s degree in Computer Science, data science or applied mathematics with a minimum of 9 years of related work experience; Master’s degree in a related discipline with a minimum of 10 years of related work experience; PhD degree in a related discipline with a minimum of 5 years of related work experience.
Expertise in Java, C/C++, and Python.
Expertise in Linux or Unix-based systems.
Substantial experience developing, generalizing, and maintaining components of operational science data systems for major JPL missions and airborne programs, including MISR, Jason-3, SWOT, AirMSPI, and HyTES.
Significant and varied experience in developing data-driven L2 retrieval techniques including aerosol and 3D cloud tomography.
Broad knowledge and understanding of the principles underlying complex and large data acquisition, in-flight calibration, and physics-based atmospheric retrievals, involving scientific remote sensing instruments.
Practical experience in Computer Vision techniques for Object Detection, instance segmentation, and multi-sensor tracking.
Extensive knowledge of applicable industry and/or academic practices and standards for analysis of remote sensing data sets in response to science needs, and reporting of related results in journals, workshops, or conferences.
Demonstrated ability to communicate scientific instruments in-flight calibration concepts to domain scientists.
Demonstrated leadership credibility with staff, management and external liaisons (NASA, partners, customers and/or sponsors).
Team player with demonstrated leadership and mentorship skills, excellent interpersonal skills, organizational skills, and communication skills.

Nice To Haves

Basic understanding of satellite orbital dynamics, satellite and sub-orbital operational modes, and constraints affecting data collection and delivery.
Generalization, maintenance, and development of operational science data pipelines and processing algorithms.
Successful history of NASA-funded proposals.
Strong record of peer-reviewed publications.

Responsibilities

Implementation of updates to the L0 processing algorithms for sensors collecting data over complex surfaces and/or multi-angle viewing geometries.
Design and implementation of software tools in support of the optical sensor calibration analysis.
Development and execution of the processing scripts in support of the testing and validation activities for L1 and L2 data products.
Leading initiatives leveraging self-supervised machine learning models and multi-sensor datasets to segment and track object instances (such as ships, wildfire fronts, smoke plumes, harmful algal blooms, and palm oil farms).
Coordinating closely with the instrument PI, Algorithm Development Teams, DAAC, and other partner SDS elements to support end-to-end data processing and operations.
Serve as a critical interface with the JPL Scientists and External Collaborators to lead and contribute to the publications and present results at the professional meetings.
Partner with JPL instrument teams in providing data processing support in strategic mission formulation activities.
Developing concepts for planning, scheduling, resource allocation, and execution of algorithms for on-board autonomous systems that interpret complex event data to take actions to achieve mission goals.
Developing machine learning systems for interpretation of remote sensing imagery and large multi-modal models for reasoning about calibration, imagery, time, patterns, and ramifications.