Senior Data Scientist

Role overview

A senior data scientist is needed to own sales forecasting and champion data-driven decision making across a sales organization. The role sits on a Sales Analytics team that partners with analytics engineers and data analysts on a scrum team to ship data products end-to-end. Reporting to a Data Science Manager, the position involves analyzing sales trends, building predictive models across product lines and regions, and turning forecasts into actionable revenue and planning recommendations.

Responsibilities

- Build statistical and machine learning forecasting models that meet forecasting needs across multiple business domains and time horizons. - Own the end-to-end machine learning lifecycle, including scoping, feature engineering, model training and testing, deployment, monitoring, and explainability. - Translate model outputs into actionable recommendations for business leaders, identifying which drivers move the sales forecast and by how much. - Contribute to self-service analytics and data tools that let business partners answer their own questions without waiting on the team. - Provide technical leadership by mentoring junior data scientists, leading design reviews and learning sessions, and helping shape the team's roadmap and standards. - Participate in decisions across the data stack, from how source data is modeled to innovating on solutions for the sales team.

Requirements

- 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 quantitative field such as Computer Science, Economics, Math, Physics, or Statistics may count toward 2 of the 5 years. - Expert proficiency in Python or R for data cleaning, manipulation, and analysis. - Deep knowledge of time series forecasting methods such as ARIMA, Prophet, or LSTM. - Proficiency with SQL for data analysis tasks. - Experience training and evaluating machine learning models using libraries like PyTorch, scikit-learn, tidymodels, or XGBoost. - Proven track record of developing and deploying machine learning pipelines in production environments such as AWS SageMaker, Databricks, or Snowflake. - Demonstrated application of software development practices including version control and testing. - Strong communication skills in writing and conversation, especially with non-technical partners. - Experience driving self-directed projects and working cross-functionally, as well as mentoring other data scientists or leading technical discussions.

Nice to have

- Experience working with Snowflake. - Familiarity with container technologies such as Docker and Kubernetes. - Background in education or edtech.