Overview
Data Scientist — San Francisco, Engineering, In office, Full-time
Company ReadyOn — an AI-native labor operating system that optimizes frontline labor with real-time matching and decision automation.
About ReadyOn
ReadyOn applies advanced AI and market-design principles to match 2.7 billion frontline workers to the right shifts in real time. The platform supports large enterprises in predicting workforce demand, dynamically matching supply, and automating staffing decisions across multi-site operations. ReadyOn serves global enterprises and is headquartered in San Francisco.
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 techniques such as ARIMA, SARIMA, Prophet, XGBoost, LightGBM, LSTM, TFT, and other modern approaches.
Analyze large-scale structured and unstructured datasets to identify trends, seasonality, anomalies, and drivers impacting forecast accuracy.
Partner with Product, Engineering, Customer Success, and Leadership to translate 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 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 data-driven culture.
Your background
BS, MS, or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, Operations Research, or related quantitative field.
4+ years of professional experience building and deploying machine learning models in production.
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.
Strong SQL skills and experience with large-scale datasets and data warehouses.
Experience building end-to-end ML pipelines, deployment, and monitoring.
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 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 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 BI 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.
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