Data Scientist

ReadyOn; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

ReadyOn in San Francisco is seeking a senior Data Scientist to design, deploy, and monitor forecasting models across workforce planning and operational metrics.

You will collaborate with product and engineering, build end-to-end ML pipelines, and mentor junior data scientists, contributing to an AI-native labor operating system.

Qualifications

• Advanced degree in a quantitative field or equivalent experience.
• 4+ years building and deploying ML models in production.
• 2+ years with time series forecasting for business use.
• Proficiency with Python and DS libraries.
• Strong SQL and experience with large data warehouses.
• Experience with MLOps and production pipelines.
• Ability to communicate complex findings to business stakeholders.

Responsibilities

• Design, build, and deploy forecasting models for workforce planning, revenue, and operations.
• Develop production-grade time series solutions using ARIMA, SARIMA, Prophet, XGBoost, and deep learning methods.
• Analyze large datasets to identify trends and drivers impacting forecast accuracy.
• Collaborate with Product, Engineering, and Leadership to translate requirements into scalable forecasting solutions.
• Build forecasting pipelines, feature engineering, model monitoring, and automated retraining.
• Design experiments to improve forecast accuracy and quantify business impact.
• Create explainable forecasting outputs and communicate insights to stakeholders.
• Collaborate with AI/ML engineers to productionize models.
• Establish governance, data quality, monitoring, and reproducibility best practices.
• Research emerging forecasting/AI tech to improve capabilities.
• Mentor junior data scientists and nurture a data-driven culture.

Skills

Time series forecasting

Machine learning

Python

SQL

MLOps

Data visualization

Stakeholder communication

Forecasting with Prophet

Education

BS/MS/PhD in Data Science or related

Tools

Pandas

NumPy

Scikit-learn

PyTorch/TensorFlow

Statsmodels

Prophet

XGBoost/LightGBM

Temporal Fusion Transformers

Job description

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.