Data Scientist - Deep Learning Focus

Sense

Sense is on a mission to innovate smart home technology, enhancing efficiency and safety through advanced AI-driven energy solutions. Positioned within a dynamic startup environment, this role offers significant growth potential for those passionate about impactful machine learning applications.

In this role you can expect to have the responsibilities:

• Design and train deep learning models for device disaggregation.
• Conduct data preprocessing and analysis for model training preparation.
• Experiment with various model architectures to optimize performance.
• Curate datasets to establish model ground truths.
• Collaborate with engineering for model deployment across platforms.
• Remain informed on deep learning advancements and best practices.
• Collaborate with senior scientists and engineers for infrastructure improvements.

This role comes with the following benefits:

• Flexible time away policy.
• Paid parental leave.
• Engagement with challenging and varied problems.
• Participation in revolutionary technology development.
• Competitive salary and comprehensive healthcare benefits.
• Opportunities for growth at a smart home technology startup.
• Office located in Cambridge, MA.
• Stock options and 401k matches up to $10k.

This role requires you to have:

• Bachelor's or Master's degree in a relevant field.
• 1-3 years of experience in data science or machine learning.
• Proficiency with deep learning frameworks like TensorFlow or PyTorch.
• Basic knowledge of neural network architectures such as CNNs and RNNs.
• Strong Python skills and experience with data science libraries.

You would benefit from having:

• Experience with large datasets.
• Problem-solving and analytical skills.
• Eagerness to learn in a collaborative environment.

Sense is dedicated to reducing carbon emissions through intelligent energy use. They support a diverse workplace and welcome underrepresented candidates, promoting an inclusive and respectful workforce. They have received various accolades, including Best Startups in Cambridge and Global Cleantech 100.