We are hiring a Senior Data Scientist for our Sales Analytics team. You will own sales forecasting and champion data-driven decision making across our sales organization. Day-to-day, that means analyzing sales trends, building models that predict sales across product lines and regions over different time horizons, and turning those forecasts into recommendations for revenue and planning decisions. You will report to our Data Science Manager and work on a scrum team with Analytics Engineers and Data Analysts, shipping data products end-to-end. You will participate directly in decisions across the data stack, from planning how source data is modeled to innovating on solutions for the sales team. Essential Responsibilities: • Develop sales forecasting excellence: by building statistical and machine learning models that meet forecasting needs across business domains. Contribute to self-service analytics and data tools so that our business partners can get answers without waiting on the team. • Own the end-to-end machine learning lifecycle: scoping, feature engineering, model training and testing, deployment, monitoring, and explainability. • Drive strategic business partnerships: translate model outputs into actionable recommendations for business leaders, including which drivers move the sales forecast and by how much. • Provide technical leadership: mentor junior data scientists, lead technical design reviews and learning sessions, and help shape the team's roadmap and standards. Required Qualifications: • 5+ years of experience in a data science role, with 3+ years focused on sales forecasting, demand forecasting, or revenue analytics. A graduate degree in a science or other quantitative field (e.g., Computer Science, Economics, Math, Physics, Statistics) may count toward 2 of the 5 years. • Expert user of Python or R for data analysis tasks (data cleaning, manipulation, analysis). • Expert knowledge of time series forecasting methods, e.g., ARIMA, Prophet, LSTM. • Proficiency with SQL for data analysis tasks. • Proficient in training and evaluating the performance of machine learning models using industry-standard libraries like PyTorch, scikit-learn, tidymodels, and XGBoost. • Proven track record of developing and implementing machine learning pipelines running in production environments like AWS SageMaker, Databricks, or Snowflake. • Demonstrated application of software development methodology and protocols, including version control and testing. • Excellent communication skills in writing and conversation, especially with non-technical partners. • Experience driving self-directed projects and working cross-functionally. • Experience mentoring other data scientists or leading technical discussions. Preferred Qualifications: • Experience working with Snowflake. • Experience with container technologies, e.g., Docker and Kubernetes. • Background in education or in edtech.