Distributed Acoustic Sensing DAS Engineer

Growing Distributed Fiber Optic sensing technology company seeks a Distributed Acoustic Sensing (DAS) Scientist to join a small, focused R&D team in Princeton NJ (hybrid remote). This includes identifying and extracting signals of interest from sensor data and discovering data features for input to algorithms and AI models.

The Distributed Acoustic Sensing (DAS) Scientist will support data scientists and field engineers in signal processing techniques, data feature discovery, and system implementation conditions regarding various DAS applications.

Responsibilities:

Use signal processing or statistical methods to build and improve data extraction
systems based on DAS applications.

Liaise with our Data Engineering and Product Management departments on
data requirements and new discoveries.

Analyze algorithms w.r.t. complexity & performance on certain tasks.

Develop algorithms for parameter estimation based on signals with additive
and multiplicative, Gaussian or non-Gaussian noise.

Build robust statistical models (frequentist or Bayesian) on DAS signals over time.

Analyze performance of algorithms in test and production settings.

Derive methods to provide performance guarantees for algorithms
in a production setting.

Qualifications:

Master s degree or higher, or equivalent technical experience in digital
signal processing, data science, sensing & instrumentation systems,
or a related field.

At least five years of proven experience related to research & development
and implementation of FOS or DAS technologies.

Strong knowledge of time domain signal processing methods and statistics.

Experience working with large structured as well as unstructured data sets.

Functional knowledge of algorithm development and training of ML / AI models.

Professional experience in coordinating with teams of data scientists, product

managers, and software engineers

Personal Experience and Character:

FIR & IIR filter design, time-frequency methods, adaptive filter design for 1D/2D/3D signal processing.

Parameter estimation & signal detection, Real-time signal processing - Control theory (stability, resonance, etc.)

Statistical modeling with non-gaussian distributions.

Robust statistics -Time series analysis & prediction.

Robust foundation in estimation theory.

Previous experience with DAS or FOS is a must.

Proficient in Python and handling and manipulating data.

Self-motivated with a strong dynamic and structured approach to problem solving.

Proficiency with machine learning tools such as TensorFlow or PyTorch is valued.

Proven ability to work with global and multi-cultural teams.

Highly compassionate and ethically driven.

Excellent technical presentation skills.

Enjoys explaining and exchanging ideas with others.

Can clearly and concisely document technical results