What it’s like being a data scientist

TL;DR

Reflections on a year as a FAANG data scientist.

Transcript

hey guys how's it going so as many of you guys may know i'm a data scientist and i've been a data scientist working in tech for a bit over a year now i previously made videos about why i think you should be a data scientist as well as reasons why you should not be a data scientist and i will link those videos somewhere over here in this video thoug... Read More

Key Insights

• The ability to learn quickly is crucial in data science, as the field is rapidly evolving and requires constant adaptation to new tools and methodologies.
• Uncertainty is a constant in data science, and professionals must be comfortable with it while effectively communicating statistical confidence to stakeholders.
• Ambiguity is common, requiring data scientists to interpret vague requests and determine the true needs of their projects.
• Research and planning are essential, as upfront preparation can save significant time and effort during project execution.
• Burnout is a risk due to the demanding nature of the job, and it's important to manage workload and take breaks to maintain productivity.
• Data science is interdisciplinary, requiring skills in computer science, statistics, business, and more, with much learning happening on the job.
• Effective communication with stakeholders is key, as technical findings need to be presented in an understandable way to influence business decisions.
• Continuous learning and adaptation are necessary, as data science projects often require knowledge beyond what is learned in formal education.

Questions & Answers

Q: What is the main skill a data scientist needs after being hired?

The main skill a data scientist needs after being hired is the ability to learn quickly. The field of data science is rapidly evolving, with new tools and methodologies emerging frequently. Therefore, the ability to adapt and acquire new knowledge on the job is more important than relying solely on what was learned in school or during interviews.

Q: How does uncertainty play a role in data science?

Uncertainty is a significant aspect of data science. Data scientists often deal with incomplete or ambiguous data, and must be comfortable working with uncertainty. They need to effectively quantify this uncertainty using statistical methods and communicate it to stakeholders in a way that is understandable and actionable, ensuring that decisions are based on informed analysis rather than assumptions.

Q: Why is research and planning emphasized in the video?

Research and planning are emphasized because they can save a significant amount of time and effort in data science projects. By spending time upfront to understand the problem, explore existing solutions, and plan the approach, data scientists can avoid redundant work and leverage existing resources or methodologies, leading to more efficient and effective project execution.

Q: What challenges are associated with ambiguity in data science?

Ambiguity in data science arises when project goals or stakeholder requests are vague or not well-defined. Data scientists must interpret these unclear directives, determine the actual needs, and develop a strategy to address them. This requires strong problem-solving skills and the ability to ask the right questions to clarify objectives and ensure that the analysis aligns with business goals.

Q: How can data scientists avoid burnout?

Data scientists can avoid burnout by managing their workload, taking regular breaks, and setting boundaries to maintain a healthy work-life balance. Since the job involves complex problem-solving and continuous learning, it is important to pace oneself, prioritize tasks, and recognize when to step back and recharge. Seeking support and communicating with managers about workload can also help prevent burnout.

Q: What role does effective communication play in data science?

Effective communication is crucial in data science because data scientists must present their findings to stakeholders who may not have a technical background. This involves translating complex technical analyses into clear, actionable insights that can inform business decisions. Good communication ensures that the value of data-driven insights is understood and utilized effectively within the organization.

Q: Why is continuous learning important in data science?

Continuous learning is important in data science because the field is constantly evolving with new technologies, methodologies, and industry practices. Data scientists must keep up with these changes to remain effective in their roles. Additionally, projects often require knowledge beyond what is covered in formal education, necessitating ongoing learning and adaptation to new challenges and opportunities.

Q: What interdisciplinary skills are necessary for data scientists?

Data scientists need a blend of interdisciplinary skills, including computer science, statistics, business acumen, and domain-specific knowledge. They must be proficient in programming, data analysis, and statistical modeling, while also understanding the business context to provide relevant insights. The ability to integrate these diverse skills is crucial for effectively solving complex problems and driving data-driven decision-making.

Summary & Key Takeaways

• The video reflects on the speaker's experiences as a data scientist at a FAANG company, emphasizing the importance of rapid learning and adaptation in the field.

• It discusses the inherent uncertainties and ambiguities in data science, highlighting the need for effective communication and stakeholder management.

• The speaker shares personal lessons on avoiding burnout, the value of research and planning, and the interdisciplinary nature of data science work.