A data scientist helps an e-commerce business turn order, customer and website data into decisions. Typical work includes product recommendations, customer segmentation (such as RFM), churn prediction, demand forecasting, pricing analysis and A/B testing. According to the US Bureau of Labor Statistics, data scientists earned a median $120,230 a year in 2025, so smaller stores often start with an analyst or a project-based engagement.
Key Takeaways
- An e-commerce data scientist collects and analyzes data, builds and tests models, and makes business recommendations, which matches the core duties the US Bureau of Labor Statistics (BLS) lists for the occupation.
- The most common e-commerce uses are recommender systems, RFM segmentation, customer lifetime value, churn analysis, demand forecasting, dynamic pricing and A/B testing.
- The BLS reports a 2025 median salary of $120,230 and projects 35 percent job growth from 2025 to 2035, so hiring full time is a significant cost.
- A data analyst can be the better first hire when the main need is reporting rather than predictive models.
- Customer data is regulated: the EU GDPR allows fines of up to €20 million or 4% of annual worldwide turnover, whichever is greater.
Working with a data scientist can bring an e-commerce business several concrete benefits. A data scientist can make better decisions about your business by analyzing your data and providing insights that you may not have otherwise considered.
Additionally, a data scientist can help you automate processes and tasks that would otherwise be time-consuming or difficult to do manually.
This level of analysis and automation can save time and money over the long run, which can make hiring a data scientist worthwhile for an e-commerce business, provided the business has enough data and clear questions to answer.
A data scientist can also help you improve your customer service by providing data-driven recommendations about how to serve your customers better.
There are four main factors to consider when looking for a data scientist:
a) The type of data you need to be analyzed
The data you need to analyze will largely determine the type of data scientist you need to hire. There are many different types of data scientists, each with its area of expertise.
b) The size of your data set
The size of your data set will also determine the type of data scientist you need to hire. If you have a large data set, you will need a data scientist experienced in working with large data sets.
c) The complexity of your data
The complexity of your data will determine the type of data scientist you need to hire. If you have complex data, you will need a data scientist who is experienced in working with complex data.
d) Your budget
Your budget will also play a role in determining the type of data scientist you need to hire. If you have a limited budget, you may need to hire a data analyst instead of a data scientist.
When finding the proper data scientist for your e-commerce business, it is essential to consider all of these factors to find the best match for your needs.
In short, working with a data scientist e-commerce can provide advantages and benefits.
Benefits of working with an e-commerce data scientist
Better decision making
A data scientist can help you make better decisions about your business by analyzing your data and providing insights that you may not have otherwise considered. Some of the decisions that a data scientist can help you with include:
- What product to sell.
- What price to charge.
- What promotions to run.
- Which channels to sell through.
Automation of processes and tasks
A data scientist can help you automate processes and tasks that would otherwise be time-consuming or difficult to do manually. Some of the processes and tasks that an e-commerce data scientist can help you automate include:
- Developing targeted marketing campaigns.
- Conducting customer segmentation.
- Generating personalized recommendations.
Improved customer service
A data scientist can help you improve your customer service by providing data-driven recommendations about how to serve your customers better. Some of the ways that a data scientist can help you improve your customer service include:
- Identifying customer pain points.
- Analyzing customer feedback.
- Developing new self-service options.
New product development
A data scientist can help you develop new products or services tailored to your customer base’s needs. Some of the ways that a data scientist can help you create new products or services include:
- Identifying customer needs and wants.
- Analyzing customer data.
- Conducting market research.
Increased sales
A data scientist can help you increase your sales by providing you with data-driven recommendations for better targeting your customers. Some of the ways that a data scientist can help you improve your sales include:
- Developing targeted marketing campaigns.
- Generating personalized recommendations.
- Conducting customer segmentation.
Cost savings
A data scientist can help you save money by automating processes and tasks that would otherwise be time-consuming or difficult to do manually. Additionally, a data scientist can help you reduce your customer churn rate by providing data-driven recommendations about how to serve your customers better.
If you are looking for ways to improve your e-commerce business, working with a data scientist can help, as long as you start with a clear business question and clean, lawfully collected data.
What Does an E-Commerce Data Scientist Actually Do?
An e-commerce data scientist works on the same core tasks as any data scientist, applied to an online store. According to the US Bureau of Labor Statistics (BLS) Occupational Outlook Handbook, data scientists typically:
- Determine which data are available and useful for a project.
- Collect, categorize and analyze data.
- Create, validate, test and update algorithms and models.
- Use data visualization software to present findings.
- Make business recommendations to stakeholders based on the analysis.
In an online store, the data usually comes from orders, product catalogs, website and app analytics, marketing campaigns, customer service tickets and reviews. The value comes from the last step on the BLS list: recommendations that someone in the business acts on. For a wider view of how this fits into a company, see how data science and business analytics shape businesses.
Common E-Commerce Data Science Techniques
Most e-commerce data science projects use a small set of well-established methods. The table below summarizes them, with definitions drawn from Wikipedia’s articles on each technique.
| Technique | What it is | Typical e-commerce use |
|---|---|---|
| Recommender system | An information filtering system that suggests the items most relevant to a particular user | “Customers also bought” and personalized product suggestions |
| Collaborative filtering | One of the two major recommender techniques (with content-based filtering); predicts a user’s interests from the preferences of many users | Recommending products liked by shoppers with similar histories |
| RFM analysis | Segments customers by Recency, Frequency and Monetary value | Choosing which customers get win-back, loyalty or VIP offers |
| Customer lifetime value (CLV) | An estimate of the net profit a customer contributes over the whole future relationship | Setting how much to spend acquiring or retaining a customer |
| Churn rate | The proportion of customers who leave a group during a set period | Spotting lapsing subscribers or repeat buyers early |
| A/B testing | A randomized experiment comparing two or more variants using statistical hypothesis testing | Testing product pages, checkout steps or email subject lines |
| Demand forecasting | Predicting the quantity of goods customers will demand over a set horizon | Inventory planning and avoiding stock-outs |
| Dynamic pricing | A revenue management strategy that sets flexible prices based on current demand | Adjusting prices for seasonality or slow-moving stock |
| Association rule learning | A rule-based machine learning method for finding relations between items in large databases | Market basket analysis and product bundles |
Example: How RFM Segmentation Works
RFM scores each customer on three questions: how recently they bought, how often they buy and how much they spend. Wikipedia’s article on RFM gives a worked example using a 1-to-10 scale: a customer who made three purchases in the last year, the latest three months ago, and spent $600 against a $500 benchmark would score R=7, F=3 and M=10.
Segments are then built from the combinations of scores. With three categories per dimension there are 27 possible segments; one well-known commercial approach uses five bins per dimension, which gives 125 segments. Businesses often merge segments that are too small to act on.
Example: Using A/B Tests Before Rolling Out a Change
An A/B test shows one group of visitors version A of a page and another group version B, assigned at random, and then uses statistical hypothesis testing to judge which performs better. A data scientist’s role is to set the sample size, choose the success metric (for example, conversion rate) and check that a difference is not just chance. For the metrics involved, see this guide to conversion rate optimization in e-commerce.
How Much Does It Cost to Hire a Data Scientist?
According to the BLS Occupational Outlook Handbook, the median annual pay for data scientists in the United States was $120,230 ($57.80 per hour) in 2025. The BLS counted 275,600 data scientist jobs in 2025 and projects employment to grow 35 percent from 2025 to 2035, with about 24,800 openings a year on average. The typical entry-level education is a bachelor’s degree.
Those figures cover salary only. Freelance, agency and consulting rates vary widely by country, experience and project scope and are not published by any official source, so compare several quotes before committing. For a comparison with analyst pay, see how much a data analyst makes.
Data Scientist or Data Analyst: Which Does Your Store Need?
The right hire depends on the question you need answered. The table below compares the two roles by the type of work involved.
| Need | Better fit | Why |
|---|---|---|
| Weekly sales, traffic and campaign reports | Data analyst | Mostly describing what already happened |
| Dashboards for the team | Data analyst | Visualization of existing data |
| Product recommendations | Data scientist | Requires building and testing models |
| Churn prediction or CLV models | Data scientist | Predictive modeling on customer history |
| Demand forecasting at scale | Data scientist | Forecasting models that need validation and updating |
Many small stores start with an analyst and add data science work once they have enough order history to model. Anyone building these skills in-house can start with online data analytics courses.
How to Start Working With a Data Scientist
- Define one business question. For example: which customers are likely to stop buying in the next 90 days?
- Audit your data. List where orders, customer records, website analytics and marketing data live, and how many months of history each holds.
- Check data permissions. Confirm you have a lawful basis to use customer data for analysis and that access is limited to the people who need it.
- Choose the engagement model. A fixed-scope project, a part-time contractor or a full-time hire, depending on how many questions you expect to ask.
- Agree on a success metric. Tie the project to a measurable outcome such as repeat-purchase rate or churn rate.
- Test before rolling out. Use an A/B test to confirm that a model-driven change actually performs better.
Privacy and Data Protection Rules to Know
E-commerce data science relies on personal data, so privacy law applies. The EU General Data Protection Regulation (GDPR), effective since 25 May 2018, also applies to businesses outside the EU that collect or process personal data of people located in the EU. According to Wikipedia’s summary of Article 83, the most serious violations can be fined up to €20 million or 4% of annual worldwide turnover of the preceding financial year, whichever is greater.
The United Kingdom kept an equivalent “UK GDPR” after leaving the EU, and California’s Consumer Privacy Act (CCPA), adopted on 28 June 2018, has many similarities with the GDPR. This is general information, not legal advice; check the rules that apply in each market you sell to.
Frequently Asked Questions
What does a data scientist do in e-commerce?
An e-commerce data scientist analyzes order, customer and website data and builds models such as product recommendations, churn predictions, demand forecasts and customer segments. The goal is business recommendations the store can act on and test.
Is hiring a data scientist worth it for a small online store?
Hiring a full-time data scientist is a large cost, with a BLS median salary of $120,230 in 2025. Small stores often get more value from a data analyst or a fixed-scope project until they have enough data and clear questions for predictive models.
What is RFM analysis in e-commerce?
RFM analysis segments customers by Recency (how recently they bought), Frequency (how often they buy) and Monetary value (how much they spend). Stores use the resulting segments to target loyalty, win-back and VIP campaigns.
What is the difference between a data scientist and a data analyst?
A data analyst mainly reports on and visualizes what has already happened. A data scientist also creates, validates and updates algorithms and models, for example to predict churn or recommend products.
Is the demand for data scientists growing?
Yes. The US Bureau of Labor Statistics projects data scientist employment to grow 35 percent from 2025 to 2035, much faster than the average for all occupations, with about 24,800 openings a year.
