Impress employers and recruiters.
Choose from hundreds of resume examples.

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

ReadyOn is seeking Data Scientists to design, build, and deploy forecasting models that predict key business and customer metrics. This role involves developing production-grade time series forecasting solutions, analyzing large datasets, and partnering with cross-functional teams to deliver data-driven experiences. The ideal candidate thrives in ambiguous, high-impact environments and is passionate about scalable machine learning modeling.

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.

Requirements

• 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.

Skills

• ARIMA
• SARIMA
• Prophet
• XGBoost
• LightGBM
• LSTM
• Temporal Fusion Transformers (TFT)
• Python
• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• PyTorch
• TensorFlow
• SQL
• MLOps
• CI/CD
• Model Versioning
• Experiment Tracking
• Automated Retraining

Location

• San Francisco

Work Type

• In office
• Full-time

Experience Level

• 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.

Education Level

• BS, MS, or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, Operations Research, or a related quantitative field.

About the Company

• 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.
• 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.
• 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.