Senior Data Scientist, Amplify

• 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

Desirable

• 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
• 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