The Essences of Product Data Science

Based on my experience in Data Science, I've observed two primary branches in the field: 1) Product-Focused Data Science: it emphasizes experiment analysis and strategic insights to enhance user experience, drive product growth, and achieve corporate goals. 2) Machine Learning-Focused Data Science: This area involves building statistical models to solve complex business problems.

Having experience in both areas, I'd like to share some key learnings, starting with product data science. This type of data science is prevalent in large companies like Google and Meta. To excel as a product data scientist, several skills and qualities are crucial:

• Strong Business and Product Sense: A deep understanding of the company's business model, stakeholders, goals, and vision is essential. More important is to develop a system thinking approach that allows you to quickly grasp and analyze the business models of different companies as you progress in your career. This adaptability is crucial in an ever-evolving business landscape and enables you to:

  • Identify common patterns across various industries

  • Recognize unique value propositions in different business models

  • Anticipate potential challenges and opportunities in new environments

  • Apply insights from one industry to innovate in another

• Strategic Thinking: Product data scientists need to think at a high level with structured and logical thinking to quickly identify potential gaps or opportunities before diving into data analysis.

  • Developing a comprehensive mental model of the user journey and funnel is often helpful.

• Technical Skills: Solid knowledge in the following areas is required:

  • Statistics (hypothesis testing, experiment design)

  • Data manipulation and aggregation (SQL)

  • Exploratory data analysis

  • Data visualization

• Storytelling and Influence: Perhaps most importantly, the ability to communicate analysis and insights effectively is crucial. You need to convey your findings to business partners (product managers, engineers) in a compelling way that influences decision-making. Remember, these stakeholders are ultimately responsible for transforming your insights into actual product features. Your ability to tell a convincing story with data can be the difference between your analysis gathering dust and driving real product improvements.