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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** READYON, INC. - **Location:** San Francisco, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Software as a Service, Continuous Integration, Data Warehousing, Database Queries, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Feature Engineering, Pytorch, Prophet, Deep Learning, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Software Version Control, Recurrent Neural Networks - **Published:** June 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f34570c1dc5ff7b4 ## About the Role Do you have experience in Time Series Analysis?, Do you have a Bachelor's degree in statistics?, * 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. ## Description * 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., * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! 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