Interested in this Data Scientist role at ReadyOn?
Apply Now →Skills & Technologies
About This Role
Data Scientist
San Francisco
Engineering
In office
Full\-time
Company Overview
ReadyOn is an AI\-native Labor Operating System that is redefining how the world’s largest enterprises manage frontline labor. Born out of a Stanford AI Lab, the company applies advanced AI and market\-design principles to one of the hardest optimization problems on earth: matching the world’s 2\.7 billion frontline workers to the right shifts, in real time.
Frontline workers now expect the same flexibility and autonomy that gig platforms provide, while large employers face relentless pressure to meet aggressive labor\-cost targets. ReadyOn bridges that divide with a system of action that predicts workforce demand, dynamically matches it to an employer’s supply of employees, and automates the thousands of staffing decisions made daily across complex, multi\-site operations.
The platform is already proven at global scale, powering labor operations for several of the world’s largest enterprises. Landmark customers include a F250 food\-service enterprise (300K employees across 16 countries; $7B\+ annual labor spend, a F500 hotel group (250K\+ employees; $5B\+ annual labor spend), a F250 entertainment operator (75K employees; $4B\+ labor spend). Across these deployments, ReadyOn has proven that scheduling was never the real problem—it was a symptom. The true challenge is how to match people and work dynamically at scale. ReadyOn solves this problem with an AI system of action that transforms labor from a fixed cost into a strategic advantage, reshaping how enterprises think about workforce design altogether.
Headquartered in San Francisco with 80 employees, ReadyOn grew 8x year\-over\-year revenue growth in 2025, driven by multiple seven\-figure Fortune 250 enterprise deployments and a rapidly expanding pipeline.
Transform How Frontline Work Runs
Enterprises struggle to manage hundreds of millions of dollars in frontline labor spend due to decades\-old software and manual processes, creating massive, avoidable costs. Frontline labor often represents 40% of the P\&L, yet the systems managing this $3 trillion market were built for static schedules and limited flexibility.
ReadyOn was founded to reject that paradigm. Staffing is not a scheduling problem; it is a real\-time supply–demand orchestration problem. ReadyOn is an AI\-native labor operating system, built from the ground up for AI agents to perform real\-time labor optimization \- much like ridesharing platforms that match drivers and riders in real time, but applied to frontline labor instead of fixed, one\-size\-fits\-all schedules.
Who’s Building It
AI is not a bolt\-on feature in our platform. Every decision, from demand forecasting to shift assignment, flows through an adaptive, autonomous decision layer that learns from operational data and continuously optimizes for cost, compliance, and worker satisfaction. Behind that system is a founding team of experts in labor markets, enterprise software, and AI\-enabled platforms:
Reza – Engineering leader who scaled enterprise systems at Google, Yahoo, and AT\&T
Dominic – Operator who optimized labor\-intensive operations in 21 countries
Mohammad – Stanford professor and leading expert in algorithmic market design
ReadyOn has already proven product–market fit with multiple multi\-million\-dollar customers, consistent expansion within existing accounts, and measurable ROI that moves stock prices.
Ideal candidates
- Data Scientists who thrive in ambiguous, high\-impact environments and naturally set technical direction for the Software and Machine Learning Engineers.
- Care deeply about clean scalable machine learning modelling techniques, and are not afraid to rethink default patterns.
- Enjoy working closely with engineering, product, design, and AI research teams to deliver new data\-driven experiences customers actually use.
- Focus on best\-in\-class modelling techniques, not just technical output, and love solving real business problems with data, services, and automation.
Responsibilities
- Design, build, and deploy forecasting models that predict key business and customer metrics across workforce planning, revenue, demand, operational, and AI\-driven decision\-support use cases.
- Develop and maintain production\-grade time series forecasting solutions using statistical and machine learning techniques such as ARIMA, SARIMA, Prophet, XGBoost, LightGBM, LSTM, Temporal Fusion Transformers (TFT), and other modern forecasting approaches.
- Analyze large\-scale structured and unstructured datasets to identify trends, seasonality, anomalies, and business drivers impacting forecast accuracy.
- Partner closely with Product, Engineering, Customer Success, and Leadership teams to translate business requirements into scalable forecasting solutions.
- Build forecasting pipelines, feature engineering frameworks, model monitoring, and automated retraining processes.
- Design and execute experiments to improve forecast accuracy and quantify business outcomes.
- Create explainable forecasting outputs and communicate insights to both technical and non\-technical stakeholders.
- Collaborate with AI/ML engineers to productionize models within ReadyOn's platform.
- Establish best practices around model governance, data quality, monitoring, observability, and reproducibility.
- Research and evaluate emerging forecasting and AI technologies to continuously improve platform capabilities.
- Mentor junior data scientists and contribute to a strong data\-driven culture.
Your background
- BS, MS, or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, Operations Research, or a related quantitative field.
- 4\+ years of professional experience building and deploying machine learning models in production environments.
- 2\+ years of hands\-on experience developing time series forecasting models for business\-critical applications.
- Strong expertise in forecasting techniques including:
+ ARIMA/SARIMA
+ Exponential Smoothing (ETS/Holt\-Winters)
+ Prophet
+ State Space Models
+ Gradient Boosting Methods (XGBoost, LightGBM, CatBoost)
+ Deep Learning approaches (LSTM, GRU, Temporal Fusion Transformers)
- Advanced proficiency in Python and data science libraries including Pandas, NumPy, Scikit\-learn, Statsmodels, Prophet, PyTorch, TensorFlow, or similar frameworks.
- Strong SQL skills and experience working with large\-scale datasets and data warehouses.
- Experience building end\-to\-end ML pipelines, model deployment, and monitoring solutions.
- Strong understanding of feature engineering for temporal data, seasonality decomposition, anomaly detection, and forecast explainability.
- Experience with MLOps tools and practices including CI/CD, model versioning, experiment tracking, and automated retraining.
- Ability to communicate complex analytical findings to business stakeholders.
Preferred Background
- Experience working in AI\-native or high\-growth SaaS environments.
- Experience forecasting workforce, staffing, recruiting, customer demand, revenue, or operational metrics.
- Prior experience building forecasting products rather than one\-off analytical models.
- Startup experience and comfort operating in fast\-paced, ambiguous environments.
What Success Looks Like
- Improve forecasting accuracy across customer deployments.
- Build scalable forecasting services that support ReadyOn's AI\-powered workforce and business intelligence platform.
- Deliver production\-ready models that directly impact customer decision\-making and operational efficiency.
If you’re looking for predictability, rigid structure, or narrow specialization, this probably isn’t the right role. This is a senior\-level position for thinkers \- who want to define the AI/ML modeling of the future AI\-native labor operating system and shape how data, AI, and backend services come together in production with the engineering team.
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 ReadyOn, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
ReadyOn AI Hiring
ReadyOn has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national 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 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.