Interested in this Data Scientist role at ESO?
Apply Now →Skills & Technologies
About This Role
ESO is a fast\-paced, growing data, technology, and research company passionate about improving community health and safety through the power of data. We pioneer innovative, user\-friendly software to meet the changing needs of today’s EMS agencies, fire departments, and hospitals.
How You’ll Support Our Mission
We are seeking a highly motivated and experienced Senior Data Scientist to join our Data Platform team. In this role, you will contribute to the design, development, and deployment of machine learning models and advanced analytics solutions that power our products and drive meaningful business outcomes. You will work closely with our engineering and research teams, collaborating on innovative projects that leverage modern ML frameworks, robust data pipelines, and scalable cloud platforms.
What You’ll Be Doing \- The Day to Day
- Design, develop, and validate machine learning models for a variety of prediction, classification, and optimization tasks.
- Collaborate with data engineers, product teams, researchers, and clinical subject matter experts to source, clean, and transform large, complex datasets for modelling and analysis.
- Conduct exploratory data analysis, feature engineering, model selection, and hyperparameter tuning using modern ML libraries.
- Implement and evaluate statistical models, supervised/unsupervised algorithms, and time series forecasting methods.
- Develop reproducible, well\-documented code using Python and industry\-standard ML frameworks (e.g., scikit\-learn, XGBoost, LightGBM, PyTorch, TensorFlow).
- Communicate findings and insights to both technical and non\-technical stakeholders through clear visualizations and presentations.
- Translate business problems into machine learning solutions and contribute to model operationalization strategies.
- Stay current with advancements in machine learning, AI, and data science practices.
Who You Are \- The Essentials
- 5\-7 years of professional experience developing machine learning and data science solutions in production environments.
- Strong proficiency in Python, including core data science libraries: pandas, numpy, scikit\-learn, matplotlib, polars, etc.
- Experience with ML model development, evaluation, and deployment workflows.
- Experience with MLOps tools and practices.
- Hands\-on experience with SQL and working with large\-scale relational datasets.
- Solid understanding of statistical modelling, data mining, and algorithm development.
- Experience working in cross\-functional teams and translating business needs into technical solutions.
- Excellent communication, problem\-solving, and analytical skills.
Who You Are \- The Desirables (It’s a plus if you have):
- Experience deploying ML models using Azure Machine Learning or other cloud\-native ML platforms.
- Familiarity with Snowflake for data access, feature storage, or model deployment integration.
Benefits \& Perks
ESO offers a comprehensive suite of benefits to promote health and financial security for our employees and their families. For full\-time employment you this includes:
- Competitive health plans (medical, dental, \& vision insurance)
- PTO (starting at 20 days) \& 12 company holidays
- 401(k) with company match
- Telemedicine service provided by ESO
- Savings accounts (FSA, HSA, DCA)
- Employee Assistance Program (EAP)
- Annual health and wellness reimbursement
- Peace of mind benefits such as life insurance, disability insurance, and worksite benefits
- Paid parental leave, new child program, \& flexible parental return\-to\-work options
- Casual office environments and unlimited office snacks and drinks
About ESO
ESO is a fast\-paced, growing data, technology, and research company passionate about improving community health and safety through the power of data. We pioneer innovative, user\-friendly software to meet the changing needs of today’s EMS agencies, fire departments, and hospitals. We’re small enough to be nimble and fun, but big enough to be a great place to work. We serve thousands of customers out of our offices across the US, Canada and Northern Ireland.
Are you ready to Make a Difference? At ESO, we believe in bringing your true self to work every single day. If you don’t match all the qualifications on the job description, we encourage you to apply anyway! We are looking for passionate, innovative, and authentic people to help drive our mission.
All offers are contingent upon a successful background check.
*ESO is committed to creating a diverse and inclusive work environment and is proud to be an equal opportunity and affirmative action employer. We invite you to consider opportunities at ESO regardless of your gender; gender identity; gender reassignment; age; religion; race; national origin; political affiliation; sexual orientation; disability; veteran status; or other non\-merit factor.*
Applicant Privacy Notice –
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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At ESO, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ESO AI Hiring
ESO has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.