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About This Role
About Ceribell
Ceribell is a medical technology company focused on transforming the diagnosis and management of patients with serious neurological conditions. The Ceribell System is a novel, point\-of\-care electroencephalography ("EEG") platform specifically designed to address the unmet needs of patients in the acute care setting, and is being used in hundreds of community hospitals, large academic facilities and major IDN's across the country. Our entire team is driven by a shared commitment to transforming the landscape of critical care through our rapid seizure detection technology, come join the movement!
Position Overview:
We are looking for a talented data scientist/algorithm engineer who is passionate about biomedical applications and has a strong background in machine learning, pattern recognition, signal processing and time\-series analysis. The successful candidate will join the data science team and will be responsible for design and development of new state\-of\-the\-art classification algorithms that will take the field of EEG neurodiagnostics to the next level.
What you'll do:
- Use time\-series analysis, statistical signal processing and machine learning techniques to design classification algorithms for various neurological indications
- Analyze data for trends and patterns, and interpret data with a clear objective in mind
- Collaborate with business and clinical stakeholders to define project needs and communicate results of the models/analytical solutions designed
- Select and implement appropriate evaluation metrics to assess the performance of algorithms and models, considering both technical accuracy and clinical relevance.
- Document and validate models and perform statistical analysis to comply with regulatory requirements for medical device algorithms
- Mentor and guide junior data scientists and interns, fostering their growth by providing technical direction, feedback, and support
- Manage multiple projects independently, effectively prioritizing tasks and aligning deliverables with key milestones to ensure timely and successful outcomes
- Stay updated with the latest advancements in machine learning and neurodiagnostics to ensure the algorithms are state\-of\-the\-art
What We're Looking For:
- PhD in Electrical Engineering, Computer Science, Statistics, or equivalent disciplines
- 7\+ years of relevant research and/or industry experience in signal processing, filtering, statistical data analysis, time\-series analysis, pattern recognition, feature engineering, machine learning and algorithm development
- Proficient in Python and/or Matlab and/or R or similar programming languages
- Experience and interest in biological signal processing and algorithm development for biomedical applications
- Strong analytical skills, detail\-oriented and collaborative
- Experience with large\-scale datasets, including preprocessing, cleaning, and handling noisy or imbalanced data in a biomedical context
- Proven ability to work independently, define scope and and manage complex technical projects from concept to deployment
- Experience with advanced machine learning techniques, such as deep learning architectures (e.g., CNNs, RNNs) and their application to time\-series data
- Strong preference for experience in neurodiagnostics or EEG data processing and algorithm development
A candidate's final salary offer will be based on their skills, education, work location and experience, and thus it may differ from the posted range. Compensation may also include bonuses consistent with Ceribell's corporate compensation plan. Note, the above description is not all\-encompassing and Ceribell reserves the right to change or modify job duties and assignments at any time.
In addition to your base compensation, Ceribell offers eligible employees the following:
- Performance\-based incentive compensation (varies by role)
- Equity opportunities
- 100% Employer paid Health Benefits for Employees
- 50% \- 70% Employer paid Health, Dental \& Vision for dependents (depending on plan selection)
- 100% paid Life and Long\-Term Disability Insurance
- 401(k) with a generous company match
- Employee Stock Purchase Plan (ESPP) with a discount
- Monthly cell phone stipend
- Flexible paid time off
- 13 Paid Holidays \+ 3 Company Wellness Days
- Excellent parental leave policy
- Fantastic culture with tremendous career advancement opportunities
- Joining a mission\-minded organization!
Application Deadline: Ongoing
Other Job Details
Ceribell reports transfers of value to health care providers (HCPs) as required by federal and state transparency laws. These laws and implementing regulations require Ceribell to provide government agencies with HCPs' names, addresses and the type of payments or other value received, generally for public disclosure. If you are an HCP and we pay or reimburse your recruiting expenses as a result of interviewing with Ceribell, your name, address and the amount of payments made may be reported to the government.
Equal Opportunity Employer
Ceribell is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth and related medical conditions), sexual orientation, gender identity or expression, national origin, age, marital status, disability, veteran status or any other characteristic protected by law. Ceribell complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Ceribell is an E\-Verify employer. Any applicant with a disability who requires an accommodation during the application process should contact [email protected] to request reasonable accommodation.
Privacy Statement
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Compliance Disclaimer
If you believe this job posting is non\-compliant, please submit a report to [email protected]. Please note that we will not respond to inquiries unrelated to job posting compliance.
Salary Context
This $184K-$200K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Ceribell, Inc, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $184K to $200K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ceribell, Inc AI Hiring
Ceribell, Inc has 1 open AI role right now. They're hiring across Data Scientist. Based in San Jose, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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