Self-assessments during Perf cycles are necessary but not fun. Here is a breakdown of steps for data scientists to write their effective self-assessments.
Step 0: Keep track of accomplishments and artifacts during the cycle
This is the most important step to preparing a self assessment, and it begins as soon as the next cycle starts. Throughout a cycle, I like to keep a running list of accomplishments with links to artifacts like change lists, docs, and colabs. Having the content to show your productivity through the cycle is critical. Having this list is an easy way to keep track of that content. When perf comes, nobody wants to spend hours chasing down that colab or doc from 6 months ago.
Step 1: Summarize the project context and your contributions
Explain what the project is, why it’s important, and your specific contributions.
Step 2: Leadership
Indicate how your contributions exemplify leadership. Here are some specific actions that show leadership:
- Identifying a critical problem
- Bringing clarity to a complex problem
- Owning the design of a solution
- Advocating for the solution to other parts of the organization
Step 3: Difficulty
Difficulty is about the problem, not necessarily about the solution. Problems are difficult when they are 1) ambiguous and/or 2) technically challenging.
Step 4: Impact
Impact can either be measurable or immeasurable. Always include measurements of impact if it can be measured. Otherwise, emphasize how your work moved the project forward or changed how decision-makers think about a problem.
Data Science Principles 