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Senior Data Scientist - Deep Learning focus

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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.

The technology team at Sense is looking for an experienced data scientist to help us build the “large language model” of electrical devices and energy.

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:

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

Qualifications:

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

Flexible time away policy
Paid parental leave.
A wide range of difficult and interesting problems to be solved.
Work with a small team of experienced entrepreneurs creating revolutionary technology.
Great opportunity to gain experience at a consumer smart home startup.
Competitive compensation and generous healthcare benefits.
A great office in Central Square in Cambridge, MA right by the Red Line
Compensation 140k to 165k
Stock Options and 401k with up to 10k match

Why Sense

Join Sense and be part of our mission to reduce global carbon emissions by making homes smart and more efficient. Our energy data and tools demystify home energy use, empower people to take command of their usage, and enable utilities to build a cleaner and more resilient grid.

Sense supports a diverse and inclusive workplace where we all learn from each other. We welcome candidates with backgrounds that are traditionally underrepresented in tech, and we strive to foster an engaging, respectful and supportive community where everyone feels empowered to do their best work. Sense is committed to be an equal opportunity employer.

Be a part of building something that will make a difference in the world.
Have a big impact at a VC-backed consumer startup that's doing big things:

Best Startups in Cambridge - Tech Tribune
"One of the world's top 100 AI companies" - VentureBeat
Clean Tech Company of the Year - New England Venture Capital Association
50 on Fire - BostInno
Top 100 - Red Herring
Best Consumer AI Technology - AI Dev World
Global Cleantech 100

Average salary estimate

$152500 / YEARLY (est.)

min

max

$140000K

$165000K

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What You Should Know About Senior Data Scientist - Deep Learning focus, Sense

At Sense, our mission revolves around creating smart homes that keep people informed and safe while contributing to a sustainable future, and we're excited to welcome a Senior Data Scientist with a deep learning focus to our innovative team. As a vital part of our technology team, this role offers you the opportunity to make a real impact on climate change by developing sophisticated deep learning models for electrical devices and energy management. You'll engage with vast datasets, transforming how homes utilize energy. Your responsibilities will span from data exploration and analysis to training advanced deep learning models and collaborating closely with our cross-functional teams to ensure these models shine in production. We're looking for someone with an advanced degree and extensive experience in data science, especially in deep learning frameworks like TensorFlow and PyTorch. Mentorship is key at Sense, and you will also guide junior data scientists in our vibrant workplace. With a flexible time-off policy, great healthcare benefits, and the chance to work in an exciting startup environment surrounded by talented entrepreneurs, you’ll be able to thrive both professionally and personally. If you’re passionate about leveraging technology to combat climate change and have the qualifications to drive impactful results, let’s shape the future of energy together at Sense!

Frequently Asked Questions (FAQs) for Senior Data Scientist - Deep Learning focus Role at Sense

What are the responsibilities of a Senior Data Scientist at Sense?

As a Senior Data Scientist at Sense, your primary responsibilities will include designing, developing, and implementing deep learning models for device disaggregation, conducting data analysis, training, and evaluating models. You will also be involved in experimenting with different model architectures to optimize performance and collaborating with teams to deploy models in production environments.