Data Scientist, Signal Processing and Machine Learning

As a Data Scientist at Verily, you will work cross-functionally to develop novel digital measures derived from physiological signals collected using wearable or mobile devices. You will apply your skills in signal processing, machine learning and generative artificial intelligence (AI) to design novel algorithms. You will work closely with cross-functional teams (hardware, software, clinical science, quality and regulatory) to plan and execute clinical studies for analytical and clinical validation of digital measures.

You will utilize big data technologies to analyze large, complex real-world datasets combining data from multiple sources (such as wearables, clinical and behavior data) and generate insights into disease progression and deliver the promise of precision health in clinical research and care delivery.

• Develop signal processing and machine learning-based digital measures from physiological signals collected by wearable sensors (such as accelerometer).
• Utilize and contribute to Verily’s big data infrastructure for deploying algorithms in production use.
• Work cross-functionally to plan and execute clinical studies for analytical and clinical validation of digital measures.
• Analyze large, complex real-world datasets involving physiological sensors, clinical and behavior data to generate scientific evidence and motivate product direction.
• Communicate highly technical results and methods clearly to cross-functional teams, both internally and externally.

• MS degree in a quantitative discipline (statistics, computer science, biomedical engineering, or similar) with 3+ years of industry experience.
• Experience developing signal processing and machine learning algorithms.
• Experience with Python and SQL.
• Experience with statistical data analysis and data visualization.

• Experience analyzing and developing algorithms using physiological signals (such as photoplethysmography and accelerometer).
• Familiarity with software engineering principles and experience deploying algorithms in production environments.
• Experience developing deep learning and generative AI based algorithms using Tensor Flow or PyTorch, and knowledge of latest architectures.
• PhD degree in a quantitative discipline (e.g., statistics, computer science, biomedical engineering, or similar).

The US base salary range for this full-time position is $119,000- $169,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.