Is product data scientist a glorified data analyst role?
Let's demystify the product data scientist role (and interview process breakdown)
Hello friends,
Is 'Product Data Scientist' just a new term for 'Business Analyst'? Not quite -- and you'll see exactly why after reading this post. I also asked ex-Google data scientist Dan Lee for a step-by-step strategy to handle interview questions for product DS. While product data scientists don't build many machine learning models, it's essential to keep updated on new developments of LLMs for their business impact. There is a free hands-on workshop on "how to build GenAI App using Llama Index" on the 25th (Mon). Register and broaden your skillset here.
Already, let's get to it!
Sooo, what exactly is a product data scientist?
A product data scientist leverages data (e.g., user activity, profile, and monetization) to inform decisions on a company's product development. Unlike traditional data scientists who might focus on a wide array of problems, a product data scientist specializes in understanding user patterns, designing AB experiments, and scoping potential areas of product improvement.
Day-to-Day: A product data scientist's day might involve:
Collaborating with product managers to understand and define key metrics.
Building dashboards to monitor the success and health metrics of a product.
Analyzing A/B test results to evaluate new features.
Using predictive modeling to forecast user growth or product adoption.
Using clustering or segmentation to create user profiles.
While there is some overlap, the role is not merely a rebrand of the data analyst role. Data analysts primarily focus on descriptive analysis that involves building dashboards and charts. Product data scientists primarily focus on predictive and prescriptive analyses that involve hypothesizing product improvements and validating ideas using experiments and modeling.
What’s the pay range? How does it compare to MLE?
The pay for product data scientists can vary widely based on factors like geographic location, company size, and individual experience. In the U.S., the average salary might range from $90,000 to $150,000 or more for senior roles.
MLEs tend to have a higher pay scale due to their specialized skills for deploying, monitoring, and scaling machine learning models in production environments. Depending on the same factors, their pay might range from $100,000 to $170,000 or more for senior roles.
However, it doesn't mean that data scientists are less valuable than MLEs because MLEs have a higher pay range. If you feel passionate about identifying patterns to advise business decisions and designing A/B tests to help the product grow, the product data scientist role is right for you.
What are the core skillsets of product data scientists?
Technical Skills: Proficiency in SQL, Python, or R, data visualization tools like Tableau or Looker, and basic knowledge of machine learning algorithms.
Statistical Analysis: Understand statistical tests, hypothesis testing, and A/B testing methodologies.
Product Sense: Ability to understand and anticipate user needs and behaviors.
Communication: Translate complex data findings into actionable insights for non-technical stakeholders.
What is the structure of the interview process?
A typical technical screen for a product data science interview is the following:
⏰ 45 to 60 minutes
💭 Senior/Staff Data Scientist or Data Science Manager
📝 Video Call with or without virtual document (e.g., Word Doc)
📚 3 to 7 cases consisting of product metrics, AB testing, and product modeling questions.
The onsite portion will consist of the same format but 2 to 3 product-focused rounds at startups and FAANG companies.
How do you prepare for product DS interviews?
Technical Prep: Review SQL queries Python/R scripting, and brush up on fundamental statistical concepts.
Case Studies: Be prepared for product-related case studies. For instance, how would you measure the success of a new feature? Or, how would you design an experiment to test a product hypothesis?
Behavioral Questions: Reflect on past projects and collaborations with product teams. Understand the product lifecycle and how data influences decision-making.
Stay Updated: Know about the company’s products and any recent features or changes they’ve made.
What kind of questions do I expect in product DS interviews?
Product-based experiments are often complex, as reflected by these case questions by three companies.👇
[Cash App Interview] Suppose a referral program is launched that offers $10 credit to a Cash App user and $10 credit to the friend once the user has signed up. How would you design an experiment to measure the effectiveness of the referral program?
[YouTube Interview] How would you measure the impact of a new video recommendation algorithm on the YouTube homepage?
[Meta Interview] The Messenger team proposes a feature that enables users to receive recent messages either unread or unresponded. How would you measure the effectiveness of this feature in an experiment?
Let's do a deep dive using the Meta Interview question above as an example. Here is a structure you can use.
1. Define the Objective
Before diving into the experiment, it’s essential to clearly define the new feature's objective. For this feature, the objective is to increase user engagement by ensuring users do not miss or neglect important messages.
2. Select Key Metrics
Primary Metrics:
Response Rate: The percentage of users who respond to unread or unresponded messages after seeing the notification.
Secondary Metrics:
Open Rate: The number or percentage of users who open the message notification.
Retention Rate: Check if users who are exposed to the feature return to the app more frequently than those who aren’t.
3. Experiment Design
Random Assignment: Split your user base into two groups:
Control Group: Users who do not receive the new feature.
Treatment Group: Users who receive the new feature.
Ensure that these groups are randomly selected and that they’re statistically comparable in terms of demographics, user behavior, etc. Set the significance level at 0.05, statistical power at 0.80, and MDE at 1% relative lift from the baseline response rate.
4. Run the Experiment
Run the experiment for 1 to 2 weeks to achieve the desired sample size, which is calculated based on the significance level, statistical power, and MDE.
5. Launch Decision
Analyze the results:
Check for statistical significance to ensure that observed differences are likely not due to chance. Check for the practical significance to see if the lift is meaningful for the business.
Consider confounding variables or external factors that might have influenced the results.
If the treatment group shows a statistically significant improvement in the key metrics without adverse effects on secondary metrics, the feature can be deemed effective. If not, further analysis or iteration might be needed.
What’s the career trajectory for product DS?
The career trajectory for a product data scientist is as follows:
Entry-Level / Junior Data Scientist:
Focuses primarily on data cleaning, exploratory data analysis, and learning company data infrastructure.
Begins to get involved in smaller product projects and may assist senior members in bigger initiatives.
Gains experience in tools and methodologies relevant to the company’s products.
Senior Data Scientist / Lead Data Scientist:
Provides leadership on major product initiatives, making high-level decisions based on data.
Mentors junior data scientists.
Collaborates with senior management and becomes a key stakeholder in product strategy discussions.
May begin to focus more on advanced modeling or experimental design.
Principal Data Scientist / Staff Data Scientist:
Recognized as an expert within the organization.
Drives innovation in data science methodologies and tools (e.g., research and develop an experimentation framework)
Often involved in long-term strategic planning and ensuring the product roadmap aligns with data-driven insights.
Data Science Manager / Director:
Manages a team of data scientists, setting priorities and ensuring team growth.
Engages in hiring and talent development.
Collaborates with other leaders in the organization on overarching business and product strategies.
Since a product data scientist operates closely with the business side, it offers opportunities for business leadership roles, making it a great career choice for various paths. I just interviewed a manager of product data science at Meta, and he transitioned from MLE to product DS because of his passion for business impact. If you want to start preparing for interviews, Dan Lee, who helped me with today's post, offers a 10% discount for you when you study his interview courses here using code "daliana10off".
What else do you want to know about the role of a product data scientist? Just hit reply and let me know!
Before you go, don't forget to register for the "Building GenAI App using Llama Index" workshop -- you'll get the recording even if you can't make it.
Until next time,
Daliana
