Leidos · 5 hours ago

Data Scientist for Energy

United States

Full-time

Remote

Entry Level

$65K/yr - $118K/yr

2+ years exp

Maximize your interview chances

ComputerGovernment

Actively Hiring

Insider Connection @Leidos

Discover valuable connections within the company who might provide insights and potential referrals.
Get 3x more responses when you reach out via email instead of LinkedIn.

Responsibilities

Responsible for handling the extract, transform, and load (ETL) for multi-domain data primarily related to electric utility and renewable energy systems

Apply various machine learning algorithms to the multi-domain data to measure the performance of the algorithm and the suitability of the training data

Work with convolutional neural nets, deep neural nets, federated learning, computer vision, reinforcement learning, autoencoders, and a host of natural language algorithms including Stanford CoreNLP, Spacy, Stable baselines3, Word2Vec, Gymnasium and Ray RLLib along with transformer models like BERT

Explore subjects like domain adaptive prototypes to examine the operational capabilities of bi-directional learning, multimodal learning, model fusion, and continuous learning both with supervised and unsupervised approaches

Other duties as assigned

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine Learning AlgorithmsPythonPytorchTensorFlowKerasReinforcement LearningNumPySciPyScikit-learnRay RLLibStable baselines3GymnasiumStanford CoreNLPData Exploration TechniquesElectrical EngineeringGraph Data for MLAutomated OptimizationRobotics

Required

Bachelor's degree in Computer Science, Data Science or related field and two plus (2+) years of relevant experience

Base understanding of machine learning algorithms, tools and platforms

Required experience in one of the following toolkits: Pytorch, TensorFlow, or Keras and working knowledge of at least three of the following: NumPy, SciPy, scikit-learn, Ray RLLib, Stable baselines3, Gymnasium, vow pal wabbit, Stanford CoreNLP.

Understanding of programming fundamentals

Python proficiency

Self-starter and intellectual curiosity both in data science and in energy/environment domains

Proficiency in data exploration techniques and tools

Willingness and interest in learning about and working with reinforcement learning