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About This Role
Data Scientist (Level depending on experience)
Remote (US)
About VivoSense
===================
VivoSense is a science\-first digital health company building the future of patient\-centered evidence through advanced biosensor analytics. We partner with pharmaceutical companies, biotechnology organizations, and academic researchers to transform high\-resolution sensor signals into validated digital measures that improve clinical trials and accelerate the development of new therapies.
Our work spans the full scientific lifecycle – from signal acquisition and algorithm development to clinical validation, regulatory evidence generation, and delivery of analysis\-ready datasets for global clinical trials.
About the Role
==================
We're looking for curious, versatile data scientists who enjoy solving difficult scientific problems.
This is intentionally an open\-level position. Whether you're an early\-career scientist eager to learn or an experienced technical leader, we're interested in people who enjoy wearing multiple hats and working across disciplines.
One day you may be validating a novel digital endpoint using clinical trial data. The next you might be developing a machine learning algorithm for ECG signals, developing production\-quality Python code for a wearable sensor pipeline or troubleshooting unexpected edge cases in real\-world sensor data.
Success in this role requires someone who enjoys ambiguity, continuously learning new technologies, and moving comfortably between science, statistics, software, and clinical research.
What You'll Do
==================
- Develop novel digital measures from high\-resolution sensor
- Design and evaluate algorithms that transform raw sensor signals into clinically meaningful outcomes through signal processing, feature engineering, and statistical
- Design and execute analytical validation and clinical validation
- Evaluate reliability, validity, responsiveness, and clinical meaningfulness of digital
- Work with multimodal physiological signals including accelerometry, PPG, respiratory bands, ECG, gyroscope, temperature, and other wearable sensors.
- Develop filtering, segmentation, feature extraction, and quality assessment
- Handle challenging real\-world data including missing data, motion artifact, signal quality issues, and edge cases.
- Develop robust Python code supporting analytical
- Build reusable analysis
- Translate raw sensor streams into structured, SDTM\-like datasets suitable for downstream statistical analysis.
- Write maintainable, well\-tested code supporting clinical trial
- Collaborate with biostatisticians, software engineers, clinical scientists, and product
- Support scientific publications, conference presentations, regulatory interactions, and client deliverables.
Who You Are
===============
We're open to a wide range of experience levels.
Early Career Candidates
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- MSc or PhD in Biomedical Engineering, Data Science, Biostatistics, Computer Science, Applied Mathematics, Physics, or related quantitative field.
- Strong programming
- Coursework or research involving signal processing, machine learning, statistics, or physiological data.
- Curiosity and desire to
Senior Candidates
---------------------
- Several years of experience developing algorithms or digital measures for healthcare or clinical research.
- Experience leading technical projects from concept through
- Experience mentoring junior
- Ability to independently design analytical approaches for novel scientific
- Experience interacting with external collaborators or
Preferred Experience
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- Python
- Time\-series analysis
- Signal processing
- Statistical modeling
- Clinical trials
- Wearable sensor data
- Digital biomarkers
- Machine learning
- Physiological and kinematic signal analysis
- Git/version control
- Data visualization
What Makes Someone Successful Here
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- Enjoys solving complex, ambiguous
- Moves comfortably between science and
- Thinks critically rather than simply applying existing
- Writes clean, maintainable
- Communicates complex technical ideas
- Takes ownership while collaborating across
Career Growth
=================
This position is intentionally open\-level. Candidates will be hired at the level (Data Scientist through Senior Data Scientist) that best reflects their experience, technical expertise, and ability to independently contribute.
As you grow, you'll have opportunities to lead scientific programs, develop novel digital measures, mentor other scientists, contribute to regulatory strategy, and help shape the future of digital endpoints.
Additional benefits and perks
- Unlimited PTO
- Board approved stock options
- Board approved annual bonus
- Direct access to senior leadership
- Healthcare
- Vision/Dental
- Life Insurance
- Professional growth training
- 401K with Safe Harbor employer contribution
- Remote work
- Flexible working hours
Salary Range: 100\-150K (DOE and Location)
VivoSense is an Equal Opportunity Employer and E\-Verify participant.
Please note: Our hiring process includes, but is not limited to, a final round in\-person interview, background check, employment verification, and professional reference checks.
This role is open only to candidates residing in the following states: AZ, CA, CO, FL, GA, MA, MD, NC, NH, NJ, NV, OH, OR, PA, TN, TX. If you do not reside in one of these states, you will not be eligible for consideration at this time. If you are not able to work in the United States without sponsorship or extensions, we will not be able to accept your candidacy for this role.
Salary Context
This $100K-$150K range is in the lower quartile 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 VivoSense, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($125K) sits 35% below the category median. Disclosed range: $100K to $150K.
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.
VivoSense, Inc. AI Hiring
VivoSense, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in San Diego, CA, US. Compensation range: $150K - $150K.
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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