Automating repetitive forecasting tasks through advanced data science techniques, including time series forecasting and human-in-the-loop workflows, to improve the accuracy and efficiency of financial predictions for Adobe's largest business unit.
Requirements
- Deep experience in time series modeling, including building, deploying, and explaining forecasting models
- Proficiency in Python programming, with intermediate to advanced skills
- Experience with data pipelining, preferably within Databricks environment
- Extensive prior experience in fully automated time series forecasting using Python
- Strong understanding of financial concepts such as ARR and ARPU
- Experience in designing human-in-the-loop workflows to enhance forecast accuracy and explainability
Responsibilities
- Build, improve, and maintain sophisticated ARR time series forecasting models to enhance accuracy and efficiency
- Automate repetitive forecasting tasks to save time and reduce manual effort
- Incorporate human-in-the-loop approaches to improve forecast explainability and accuracy
- Identify and pursue additional high-impact data science opportunities within the FP&A domain
- Contribute to dashboard development and other visibility tools to support strategic decision-making
- Serve as a data science evangelist, promoting best practices and innovative solutions across teams
- Stay current with emerging data science techniques and financial modeling trends to continuously enhance forecasting capabilities
Other
- Collaborate with finance partners to understand forecasting needs and design tailored solutions
- Present methodologies, insights, and results to stakeholders across various levels, from analysts to SVPs
- Excellent problem-solving skills with the ability to articulate issues and recommend solutions
- Exceptional communication skills, capable of presenting complex methodologies to non-technical audiences
- Ability to collaborate effectively with cross-functional teams, both local and remote
