What is Product Data Science?

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data.

• 70% of Data Science Jobs are Product Data Science.
• Blend of Data Scientist and Product Analyst/Growth Analyst
• We build the data pipelines and infrastructure, create tools to query and visualize it, analyze experiments and trends, and develop algorithms that feedback into the product. Work as part of a cross-functional team of product, data science, tech, design, and user research. We have one foot in engineering and the other in product development.
• We work together to analyze patterns at a macro level, answering questions the rest of the company hasn’t thought to ask yet

Analytical Thinking

1. How to grow and shape data tools and datasets to improve data-driven decision making
2. Passion to translate business objectives into actionable analyses and results in business and product recommendations
3. Research international markets, growth marketing, web platforms
4. Communicating the results of analyses to leadership teams to influence the strategy

Skills & Resources

Product Insights

• Logging and Instrumentation. Identifying Data Gaps. Establish good relationships with engineers
• Push Notification Analysis — How many users are eligible for push notifications? across user segment? across clients? What are the tap rates of different push notification types?
• SMS Delivery Rates — How do we calculate Twitter’s SMS delivery rates across different carriers? Are our delivery rates in emerging countries poorer? How can we make them better?
• Multiple Accounts — Why do certain countries have a higher ratio of multiple accounts? What drive people to create multiple account?
• More importantly, you will slowly move from a reactive state to a proactive state and start suggesting interesting analyses that product leaders might not think of because they don’t know the data exists or that disparately different data sources can be complementary and combined in a particular way
• Python and SQL

Resources