Instead of simply copying notebooks, a great data scientist would:
- Create original solutions tailored to specific business problems
- Deeply understand and properly document methodologies
- Build upon others' work while adding significant improvements
- Contribute back to the community with novel approaches
Rather than training models blindly, they would:
- Thoroughly explore and understand the data before modeling
- Perform comprehensive exploratory data analysis
- Identify and address data quality issues proactively
- Document data lineage and characteristics meticulously
Instead of optimizing solely for accuracy, they would:
- Balance multiple metrics relevant to business objectives
- Consider trade-offs between accuracy, interpretability, and efficiency
- Optimize for business impact rather than technical perfection
- Develop solutions that address stakeholder priorities
A great data scientist would validate assumptions by:
- Testing hypotheses rigorously before proceeding
- Implementing systematic cross-validation approaches
- Conducting sensitivity analyses on key parameters
- Challenging their own biases and preconceptions regularly
Rather than avoiding stakeholders, they would:
- Actively engage with business partners throughout projects
- Translate technical concepts for non-technical audiences
- Seek diverse perspectives to better understand requirements
- Build relationships across departments to enhance collaboration
Instead of remaining stuck with basic tools, they would:
- Continuously learn and adopt new technologies when appropriate
- Move from exploration to production-ready implementations
- Develop end-to-end solutions that deliver tangible value
- Create reproducible and maintainable workflows
Rather than just delivering charts, they would:
- Provide actionable insights with clear recommendations
- Develop models that drive measurable business outcomes
- Implement solutions that automate decision processes
- Quantify the impact of their work in business terms
Instead of ignoring industry shifts, they would:
- Strategically incorporate emerging technologies like LLMs
- Balance innovation with practical implementation
- Stay current with advances while focusing on business needs
- Leverage new tools to solve previously intractable problems