Section 1.1: Understanding the Capability Framework

Before delving into my performance review, it’s beneficial to outline the expectations for a graduate data scientist. Within my team, we utilized a capability framework that identifies the essential skills and competencies required at various career stages.

This framework emphasizes three key domains that define a well-rounded data scientist: problem-solving, communication, and interpersonal skills.

1. Problem-solving
   • Framing: Planning tasks effectively and breaking down problems into manageable analytical steps.
   • Analyzing: Conducting thorough quantitative analyses, which includes data collection, verification, and proficiency in Excel and SQL.
   • Assessing: Presenting findings succinctly through tables or charts.
2. Communicating
   • Written: Crafting clear emails and PowerPoint presentations.
   • Oral: Engaging in effective, task-oriented discussions.
3. Relating
   • External: Collaborating effectively with clients.
   • Internal: Contributing positively in team settings.

Section 1.2: Insights from My Performance Review

My review followed a project where I collaborated with a state government on COVID-19 response initiatives, specifically in developing dashboards that relayed public health information. As part of my preparation for the review, I was asked by my performance manager to create a list of my strengths and areas for improvement throughout the project.

This exercise was designed to evaluate my self-awareness and see how my perceptions aligned with the feedback from my colleagues. Overall, my self-assessment was largely in agreement with their comments.

While I felt confident about my technical skills—such as writing SQL and building Power BI dashboards—I struggled with personal motivation during the project, which affected my collaboration and engagement in team meetings.

Here’s a summary of the feedback from my performance review:

Strengths

• Capable of coding complex logic.
• Proficient in quantitative analysis and SQL tasks.
• Trusted with creating and fixing dashboards.

Weaknesses

• Improve sense-checking of answers and best practices in coding (e.g., writing clearer comments).
• Develop a deeper analytical mindset and formulate hypotheses.
• Increase collaboration and engagement during team discussions.

Lessons Learned and Reflections

Despite anticipating negative feedback, it was still challenging to hear. However, I continuously reminded myself that recognizing areas for improvement is vital for growth. By acknowledging my shortcomings, I can focus on what needs to be enhanced moving forward.

The insights from my performance manager indicated that my feedback was typical for recent graduates: strong technical skills, yet lacking in soft skills like communication and teamwork. While I quickly adapted to using SQL and Power BI, my motivation waned during the project, affecting my overall performance.

For instance, I could have been more diligent in documenting my code, making it easier for team members to understand my intentions. Additionally, I needed to be more proactive in forming hypotheses and validating my results.

To address my feedback, my manager offered several pieces of advice. He suggested that I start each analysis with a high-level plan, which would clarify my understanding of the problem and guide my approach. This strategy not only helps in structuring my work but also opens the floor for colleague feedback before I dive into coding.

To enhance my participation in meetings, my manager advised summarizing my understanding at the end of discussions. This practice ensures I remain engaged and confirms my grasp of the tasks at hand.

I hope this brief reflection emphasizes the importance of embracing feedback and provides valuable insights from my performance review experience. Constructive criticism is essential for self-discovery and continuous improvement in our professional lives.

The first video, "How to Deal with Failures (in Interviews) as a Data Scientist," offers strategies for overcoming setbacks in the data science field.

In the second video, "Why You Should NOT Be A Data Scientist," the speaker discusses the challenges and considerations of pursuing a career in data science.