Staff Machine Learning Data Scientist - Women's Health

ŌURA; San Francisco (CA); Remote; USD 150,000 - 200,000; Full time

Job summary

A leading company in health technology is seeking a Staff Machine Learning Data Scientist to enhance women's health outcomes through innovative machine learning models. This role involves leading projects, mentoring, and collaborating with cross-functional teams. The position is primarily remote, with occasional on-site meetings in San Francisco. Competitive salary and benefits are offered.

Benefits

Health benefits

Paid time off

Holidays

Wellness days

Sick leave

Parental leave

Employee discounts

Oura Ring

Qualifications

• 7+ years of relevant experience in related fields.
• 5+ years of advanced programming experience.

Responsibilities

• Lead development of machine learning pipelines for Women's Health.
• Collaborate with teams to commercialize machine learning models.
• Mentor junior data scientists.

Skills

Python

SQL

Leadership

Communication

Education

PhD in biomedical engineering

PhD in electrical engineering

PhD in biostatistics

PhD in computer science

Job description

Staff Machine Learning Data Scientist - Women's Health

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Job Description

At Oura, our mission is to empower every person to own their inner potential. With our award-winning Oura Ring and app, we help over 2.5 million people turn insights about sleep, activity, and readiness into healthier, more balanced lives. We foster a culture of support, inclusion, and inspiration, guided by our core values.

We have offices in San Francisco and San Diego for hybrid or in-office work. Employees in other major cities like Boston and New York may gather informally at local co-working spaces.

We are seeking a Staff Machine Learning Data Scientist to join our Women’s Health team. You will develop and commercialize cutting-edge machine learning models using wearable health data to enhance women’s health outcomes. You will lead projects, mentor junior staff, and influence our strategic technology direction.

Responsibilities

1. Lead the development and deployment of advanced machine learning pipelines and models for Women's Health features, focusing on time-series analysis and validation.
2. Collaborate with cross-functional teams to commercialize machine learning models and algorithms.
3. Design and perform statistical analyses to evaluate model performance and generate insights for product strategy.
4. Research and develop novel health sensing features, translating concepts into scalable products.
5. Mentor junior data scientists and establish best practices for machine learning within the team.
6. Communicate complex technical concepts to diverse audiences, including leadership and stakeholders.

This is a remote role within the US, preferably based in San Francisco. Occasional on-site meetings in San Francisco may be required.

Minimum Requirements

• PhD in biomedical engineering, electrical engineering, biostatistics, computer science, or related field with 7+ years of relevant experience. Healthcare experience is a plus.
• 5+ years of advanced programming in Python and SQL.
• Extensive experience with machine learning, deep learning, time-series analysis, and signal processing, including deploying models commercially.
• Proven leadership in mentoring, project management, and influencing technical direction.
• Strong communication skills for technical and non-technical audiences.
• Domain knowledge in Women’s Health and wearable health data is advantageous.
• Ability to work with a global team, with occasional meetings with European colleagues and travel up to 3 times a year.

Benefits

We offer competitive salary and equity, health benefits, an Oura Ring, employee discounts, paid time off, holidays, wellness days, sick and parental leave. Salary ranges vary by region, with specific tiers outlined in the job listing.

Oura is committed to diversity, equity, and inclusion, and is an equal opportunity employer. We provide accommodations for applicants with disabilities and advise caution against fake job offers.