Senior Data Scientist - Deep Learning focus

Job Description

Senior/Principal Data Scientist - Deep Learning Focus

About Sense:

Our mission at Sense is to make all homes intelligent by keeping people informed about what's happening in their homes, and helping to make homes safer, more efficient, and more reliable. At Sense, we are serious about having a real impact on climate change.

About the Role:

We are seeking a motivated and experienced Senior/Principal Data Scientist to join our team. You will play a crucial role in developing and deploying state-of-the-art deep learning models to solve challenging problems in time series analysis and energy. You will have the opportunity to work with large, complex datasets and contribute to the entire model development lifecycle, from data exploration and preprocessing to model training, evaluation, and deployment. Models are deployed to both cloud and embedded systems where they power our consumer application and real-time embedded applications on electrical meters.

Responsibilities:

1. Design, develop, and implement deep learning models for device disaggregation.
2. Conduct thorough data analysis and preprocessing to prepare data for model training.
3. Train and evaluate deep learning models using appropriate metrics and techniques. Contribute to curation of ground truth.
4. Experiment with different model architectures and hyperparameters to optimize model performance.
5. Collaborate with a cross-functional team to deploy, maintain, and support deep learning models in production environments.
6. Stay up-to-date with the latest advancements in deep learning research and technologies.
7. Contribute to the development of our data science infrastructure and best practices.
8. Mentor and guide junior data scientists.

Requirements

Qualifications:

1. Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, Statistics, Electrical Engineering, Computer Engineering, or a related field.
2. 5+ years (Senior) / 8+ years (Principal) of experience in data science, with a strong focus on deep learning.
3. Experience in training and deploying deep neural networks using popular frameworks such as TensorFlow, PyTorch, or Keras.
4. Solid understanding of deep learning architectures (CNNs, RNNs, Transformers, etc.) and their applications.
5. Proficiency in programming such as Python and experience with relevant libraries (e.g., NumPy, Pandas, Scikit-learn).
6. Experience working with large datasets.
7. Strong communication and collaboration skills.
8. Must be authorized to work in the U.S.

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