NRG Systems

Job 169845 - Data Scientist
Hinesburg, VT

  Sign In Job List   

Job Details

Location: Hinesburg, VT
Employment Type: Full-Time
Salary: Competitive

Job Description

Key Focus

 

The Data Scientist will support product development projects that require a solid background in engineering, statistical analysis, data comprehension and/or computer science. The position focuses on developing products or services for customers that might include such things as “big data” services, extracting knowledge from disparate data streams, understanding and detecting complex machine or sensor fault modes, and proposing prognostic solutions using large datasets. The Data Scientist will be involved in core algorithm development for NRG products, including those related to Remote Sensors (LIDAR and SODAR) as well as meteorological instruments. The scope of responsibility will include data analysis to support product conceptualization, product definition, product design and development, operational support, test and verification, and development of services for customers. The Data Scientist will also represent NRG Systems as appropriate to influence relevant industry and market decisions.

 

The Data Scientist is a full-time position in the engineering department and reports to a Functional Engineering Manager. This person will work in partnership with NRG’s dynamic engineering team to achieve goals of the department that are in line with the company strategic plan, and will embrace the high standards of NRG ethics and core values.

 

Job Responsibilities

• Data structuring, interpretation, and analysis of large data sets from LIDAR, meteorological sensors, acoustic and vibration sensors, and SCADA data using advanced mathematical models, statistics, and machine learning methods.

• Signal processing of LIDAR data including development of algorithms, advanced filters, and domain transformation.

• Data cleaning, analysis, interpretation, visualization concepts and prototypes, and predictive analytics for meteorology and wind energy applications using statistics and machine learning methods.

• Design, development, and test of fault detection hardware or algorithms and models for new and existing sensors and systems.

• System fault feature/signature extraction based on expert domain knowledge of discrete signal/event analysis.

• System fault pattern recognition using new/existing features.

• Product idea innovation.

• Technical presentations and papers for industry conferences.

• Technical presentations in support of marketing NRG products.

• Supporting the development of document content for NRG Systems’ global industry markets.

• Developing intellectual property for NRG Systems.

• Interfacing with Suppliers and Customers.

• Hands-on prototyping and field testing.

Requirements

Qualifications

 

The candidate should have an M.S. in Meteorology, Electrical Engineering, Mechanical Engineering, Engineering Statistics or Computer Science with three (3) years of academic or industrial experience in LIDAR/SODAR remote sensing, machine learning, and signal processing.

 

Technical Skills:

• Minimum of 3 years statistical analysis experience specializing in signal processing and machine learning

• Strong domain knowledge of MATLAB and Python

• Practical experience with R a plus

• Strong data science domain knowledge, with a track record of applying unsupervised and supervised learning methods

• Experience applying feature extraction and pattern recognition techniques

• Experience processing and analyzing large data sets

• Advanced digital signal processing and filtering capability

• Experience in numerical modeling

• Experience in image processing helpful

• Proficiency with engineering design tools and processes helpful

• Proficient skill and experience in making technical presentations

 

Other Skills

• Excellent communication skills and a team player

• Independence, self-direction

• Ability to be innovative

• Ability to multi-task and meet deadlines

• Ability to integrate work into complex systems

Send to a Friend