Senior Data Scientist

Prisma photonics helps to keep the most critical large-scale infrastructure running with the next generation of fiber sensing monitoring. Paving the way to smarter, safer, efficient, and environmentally friendly linear infrastructure. In the domain of overhead power lines, scalable fiber sensing offers a huge potential to transform electricity networks to smart, data-driven grids with increased capacity and by this to impact the world’s transition to renewable energy. 

We look for the best minds and spirits to join us in our quest to make big things run

better. We balance cutting-edge optical fiber laser technology with

forward-looking machine learning and algorithmic research. We embrace a

data-driven paradigm that takes full advantage of the unprecedented richness

and quality of the data captured by our sensors, allowing us to develop

state-of-the-art algorithmic engines and machine learning models to turn data

into decisions.

About You

We’re looking for a top-notch senior machine learning researcher, with the

experience, skills and data-driven reasoning to take a leading role in solving

our core data science challenges. To meet these, You will be expected to

exhibit outside-of-the-box thinking and leadership skills, while collaborating

with the engineering, hardware, data and field application teams.

As a member of our Power team at Prisma Photonics, you will tackle unique challenges

in the domain of Optical Fiber Sensing in electric power lines. Fiber Sensing

data measures the acoustic signal along an optical fiber which can be

interpreted as a continuous acoustic signal originating from tens of thousands

of microphones spanning over hundreds of kilometers. You will cultivate a deep

familiarity with this domain and research novel ML approaches that address

domain specific challenges.

Responsibilities:

•  Devise, initiate and manage machine learning projects for solving challenging problems in the Power domain.
• Develop cutting edge models for detection, classification, time series forecasting, and more.
•  Use advanced data analysis and visualization tools to gain insights from diverse real-world physical data.
• Proactively seek new directions for tackling hard-to-crack problems, including devising real-world experiments and data collection, and collaborating with fellow physicists and engineers.
• Learn new topics and conduct literature reviews to gain deep familiarity with relevant physical domains, such as optics, acoustic vibrations and weather forecasting.
• Work in a dynamic environment of short life cycle research and development.
• Work in a dynamic environment of short life cycle research and development.