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Staff Scientist United States Jobs (Remote work)

16 Job Offers

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Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection)
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Lead ML and bioinformatics research for multi-cancer early detection at Natera. Build interpretable deep learning models on multi-modal genomic data, drive methods from prototype to clinical validation, and mentor scientists. PhD plus 6-12 years required.
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Natera
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Until further notice
Senior Staff Data Scientist - Consumer Experimentation
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232500.00 - 325500.00 USD / Year
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Reddit
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Staff Data Scientist
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180000.00 - 195000.00 USD / Year
Evolve
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Staff Scientist, Bioinformatics/RWD
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Not provided
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Natera
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Staff Machine Learning Scientist, Translational AI
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Natera
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Staff Data Scientist– Pricing Science
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Not provided
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Sur La Table
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Staff Data Scientist, Marketing
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217000.00 - 303900.00 USD / Year
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Reddit
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Staff AI Scientist
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Oura
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Clinical Research Staff Scientist, Real World Evidence
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Oura
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Senior Staff Machine Learning Scientist, Assets
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Not provided
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Webflow
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Staff Data Scientist, Ads
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217000.00 - 303900.00 USD / Year
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Reddit
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Staff Product Data Scientist, Lending
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Cash App
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Staff Product Data Scientist, Lending
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Not provided
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Cash App
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Staff Data Scientist, Pricing
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Location
United States, Bay Area
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Salary
239600.00 - 359400.00 USD / Year
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Block
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Staff Product Data Scientist, Lending
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Cash App
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Senior Staff Data Scientist
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Mozilla
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About the Staff Scientist role

Staff Scientist jobs represent a high-impact, senior-level career path that sits at the intersection of advanced data analysis, machine learning, and strategic business decision-making. Professionals in these roles are typically responsible for tackling the most complex and ambiguous problems within an organization, moving beyond routine analytics to drive innovation and long-term technical direction. A Staff Scientist is often viewed as a thought leader who not only executes sophisticated analyses but also proactively identifies new opportunities for growth, efficiency, or risk mitigation.

The core responsibilities of Staff Scientist jobs generally revolve around designing and implementing robust models and experiments. This includes working with massive, high-dimensional datasets to uncover hidden patterns, build predictive systems, and optimize for rare but critical events. A significant portion of the work involves advanced experimentation, going far beyond simple A/B testing to include causal inference methods like difference-in-differences, synthetic controls, and marketplace-specific designs such as switchback experiments. Staff Scientists are expected to own the full lifecycle of a project—from framing the initial problem and defining success metrics to deploying production-ready algorithms and communicating findings to senior leadership. They often serve as a bridge between technical teams and business stakeholders, translating complex quantitative results into clear, actionable narratives that guide product strategy and resource allocation.

Typical skills for Staff Scientist jobs include deep expertise in statistical modeling, machine learning (particularly classification and anomaly detection), and experimental design. Proficiency in programming languages like Python and SQL is non-negotiable, as is the ability to work with large-scale data infrastructure. Beyond technical chops, these roles demand exceptional strategic thinking and cross-functional leadership. Staff Scientists frequently mentor junior team members, set the scientific agenda for their group, and influence decision-making at the director or VP level. They must be comfortable operating in ambiguous environments where classical testing frameworks may not apply, requiring them to design novel measurement approaches. Educational backgrounds commonly include advanced degrees (Master’s or Ph.D.) in quantitative fields such as statistics, economics, operations research, or applied mathematics, though significant industry experience can sometimes substitute.

In essence, Staff Scientist jobs are for those who want to combine deep technical rigor with broad business impact. These professionals do not just answer questions—they define which questions are most important to ask, ensuring that data science drives tangible, measurable outcomes across the entire organization.