Buoy Health
Boston
51-200 employees
AI-driven digital assistant that helps people self-diagnose and get the right care at the right time.
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Lead Data Scientist
Lead Data Scientist
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We’re the best way to get better.
About Buoy Health:
Buoy builds a digital health tool that helps people – from the moment they get sick – start their health care on the right foot. Started by a team of doctors and computer scientists working at the Harvard Innovation Laboratory in Boston MA, Buoy was developed in direct response to the downward spiral we’ve all faced when we attempt to self-diagnose our symptoms online. Buoy leverages artificial intelligence – powered by advanced machine learning and proprietary granular data - to resemble an exchange you would have with your favorite doctor – to provide consumers with a real-time, accurate analysis of their symptoms and help them easily and quickly embark on the right path to getting better. Buoy is based in Boston and was founded in 2014
About the role:
As a Lead Data Scientist, your core responsibility is to leverage Buoy data to solve problems intelligently and creatively. You will work with Buoy's product and medical teams to improve the accuracy and efficacy of Buoy's AI. You will apply both classical and modern machine learning techniques. You will formulate hypotheses and design experiments to evaluate them. You will develop, specify, and implement validation methods that scale. You will communicate insights about user behavior, agent behavior, data quality, and data completeness to different teams across the company. To be successful, you must have a keen interest in data-driven decision making and helping people make better decisions about their health.
Responsibilities:
-- Plan and execute on the delivery of scalable machine learning pipelines to support Buoy’s core products
-- Ensure models running in production are testable and well-monitored
-- Mentor analysts, data scientists, and other engineers in subjects pertaining to data science and machine learning
-- Work closely with product and clinician teams to plan projects and structure work in an Agile development process
-- Identify areas of innovation, formulate research hypotheses, and rapidly prototype new models
-- Design, formulate, validate, and clearly document experiments to test models and solutions
Minimum Qualifications:
-- BS or graduate degree in Computer Science, Statistics, or related
-- At least 5-7 years professional experience as a machine learning engineer or data scientist
-- Experience in both rapid prototyping and experimentation, as well as in bringing machine learning models into a production environment
-- Deep knowledge in one or more of the following areas: reinforcement learning, deep learning / artificial neural networks, recommendation systems, or generative adversarial network, and natural language processing
-- Experience with one or more programming languages used for machine learning, preferably Python
-- Excellent written, spoken, and visual communication skills
-- Ability to formulate effective hypothesis and design testing experiments
-- Ability to define relevant metrics that can serve as appropriate and accurate measurements and lead to actionable insights
Preferred Qualifications:
-- Graduate degree in Computer Science, Statistics, or related
-- Experience in personalization, consumer behavior, HCI, and/or healthcare
-- Experience with PyTorch, SageMaker, or TensorFlow
-- Familiarity with data visualization techniques
Benefits:
-- Stock Options
-- Unlimited PTO
-- Medical, Dental, Vision
-- 401k with matching
-- Dogs in the office!
-- Half day Fridays
**This role is located in the United States. Unfortunately, we are unable to support international applicants at this time.
This job is no longer open