PyData Chicago August 2024 Meetup

Cross-functional skills combining business and technical knowledge are crucial for career advancement in data science and quantitative finance

Front office quant finance roles have high turnover (~80% leave within 5 years) due to stress, long hours, and demanding schedules including overnight/weekend work

LLMs excel at transformation tasks and code assistance but struggle with specialized domain knowledge and uncommon edge cases - they work best for consensus-based tasks

Data cleaning, transformation and visualization typically consume 90% of a data scientist’s time, while only 10% is spent on actual analysis and modeling

Domain knowledge is critical but not required at entry level - what matters is willingness to learn the business context and industry-specific knowledge

Communication skills and ability to explain technical concepts to non-technical stakeholders are essential for career growth

Both Python and R have their strengths - R excels at statistics while Python offers general-purpose capabilities. Knowing multiple languages is recommended

Creating a digital footprint through GitHub, certifications, and networking at industry events helps with career advancement

Most business problems don’t require complex ML models - simpler statistical approaches are often sufficient and preferable

Data scientists need to focus on delivering business value and answering “So what?” rather than just building sophisticated models