FP&A Today Data Science masterclass with Shifra Isaacs
May 28, 2025
Shifra Isaacs, a Developer Relations Advocate at Ascend.io with a rich background in data science and finance, shares her insights for finance professionals. She discusses the distinction between data science and business analytics, emphasizing the power of AI in enhancing decision-making. Shifra also dives into Python’s role in Excel, effective modeling techniques for risk and variance analysis, and the importance of adapting to an AI-driven landscape. Additionally, she highlights the synergy between technical skills and storytelling in finance for improved forecasting.
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Finance Roots Inform Data Science
- Shifra Isaacs started in finance at Rutgers Business School before transitioning into data science.
- Her background helps her bridge the language gap between finance stakeholders and data scientists.
Understand Data Science Lifecycle
- Data science involves ingesting, cleaning, modeling, deploying, and monitoring data and models.
- Maintaining models and monitoring for data drift is critical and often the hardest phase.
Excel Limits Model Maintenance
- Excel isn’t built for software engineering workflows like continuous integration and deployment.
- This causes issues such as broken formulas and lack of automated testing unlike in engineered data science pipelines.