What Is a Machine Learning Scientist? (Updated for 2024)

The title Machine Learning Scientist gets thrown around a lot, and it’s often confused with Data Scientist, but anyone who specializes in machine learning knows there’s a clear difference.

Unlike a data scientist, a machine learning scientist is often a research and development role. The machine learning scientist typically focuses on researching new ML methods and algorithms and generating new or improved ways for a company to utilize machine learning techniques.

For example, at Amazon, machine learning scientists are responsible for:

“Researching and developing algorithms that are used in adaptive systems across Amazon. They build methods for predicting product suggestions and product demand, and explore Big Data to automatically extract patterns.”

Ultimately, the role and title vary by company. At Facebook, for instance, they’re called Research Scientists, and at Microsoft, they’re known simply as Researchers. You’ll also find a lot of machine learning scientists in academia.

But no matter the industry, the job role is similar: researching and developing new and existing ML techniques.

Machine learning engineers and scientists share a lot of the same skills. Both roles require in-depth knowledge of algorithms, Python and SQL, as well as software engineering. Yet, there are key differences in both job function and skillset:

Job Function

A machine learning engineer deploys machine learning algorithms and models, and maintains and scales ML models in production.

A machine learning researcher, on the other hand, focuses on advancing a niche subject domain within machine learning, like natural language processing, deep learning or computer vision, or finding a new approach to a business problem. For example, a ML scientist might be responsible for modifying an existing ML library, or writing and developing a new library.

Skills

Machine learning engineers and scientists require a lot of the same technical skills: Python, SQL, algorithms, etc.

The key difference is that machine learning scientists tend to have strong backgrounds in research (which is why many are PhDs). They must know how to conduct experimental and quasi-experimental trials, and they’re skilled at documenting and presenting research.

Another difference is that machine learning researchers tend to have more specialized ML knowledge within a particular domain, like probabilistic models or the gaussian process.

Data scientists and machine learning researchers share many of the same job functions. In fact, in some companies, machine learning scientists are called simply data scientists.

But there are some key differences between the roles.

Data scientists, for example, are usually responsible for building models and presenting results to stakeholders. Their key goal is deriving business value from data, whereas in many research roles, the goal is completing a study and getting insights from research.

Although there is an overlap in skills, research roles also tend to require:

• A PhD
• More specialized backgrounds (Robotics, physics, AI or computer vision)
• Experience with experimental design
• Software engineering skills (like C++ or Java)

Ultimately, the researcher is usually singularly focused on a complex problem, like improving self-driving tech, and therefore, they tend to have a specialized background in that domain area. A data scientist, on the other hand, tends to have broad knowledge in data science, but not necessarily deep domain expertise.

These roles almost always require a PhD. In fact, we conducted an analysis of LinkedIn profiles of machine learning scientists and researchers. We found that:

• 93%+ had a PhD (most commonly in computer science, statistics, mathematics or machine learning)
• 95% had a master’s degree
• On average, ML researcher jobs require 5-7 years of experience

This isn’t always the case. For example, research scientist roles at Toyota require a bachelor’s or master’s in a quantitative field, while a Ph.D. in machine learning, robotics, or computer vision is a preferred qualification.

Transitioning from Academia

Many ML scientists make the switch from academia. In fact, almost all FAANG companies hire extensively from Ph.D. programs.

For some, it can be a tough transition, and PhDs should be prepared for a number of cultural and technological differences between university and private company research environments. They include:

• Collaboration - Ph.D. candidates tend to work in small teams or alone. In private companies, the ability to collaborate with diverse stakeholders is a necessity.
• Data - PhDs often work with fixed datasets and might not even deploy their model at scale. As an ML researcher, the model must be tested, scaled, and monitored long-term. The datasets are also constantly evolving.
• Changing Goals - In academic research environments, the goal is to generate the research result. You start with a problem statement and study it. In business, the project may evolve as the needs and leadership change within the company.

Ultimately, many from an academic background tend to enjoy private research environments, as they’re continually challenged and paid well to work on really interesting, cutting-edge tech.

Here’s a look at the average salary by role:

Interviews for machine learning roles tend to dive deep into ML techniques and methodologies. You’ll face ML algorithm questions and Python ML questions, as well as machine learning system design and case studies questions.

Here are some examples of the types of questions you might face in a machine learning interview:

1. How would you interpret coefficients of logistic regression for categorical and boolean variables?

The sign of the coefficient is important. If you have a positive sign on the coefficient, then that means, all else equal, the variable has a higher likelihood of having a positive influence on your outcome variable.

2. Write a function compute_deviation that takes in a list of dictionaries with a key and a list of integers and returns a dictionary with the standard deviation of each list.

Note: This should be done without using the NumPy built-in functions.

Before you jump into this deviation coding problem, first define how you will compute the standard deviation without using the NumPy function. This means we have to build a function to calculate the standard deviation through the formula.

3. Write a function decreasing_values to return an array of integers so that the subsequent integers in the array get filtered out if they are less than an integer in a later index of the array.

This Python array problem is difficult because it seems like it requires logic around addition and deletion from an array. The problem states that we want continuous decreasing values from the first element in the array until the end.

4. How would you tackle multicollinearity in multiple linear regression?

Multiple linear regression is a method that uses several independent variables to predict or explain the dependent variable we are interested in. When using this technique, we assume that the independent or explanatory variables are also independent of one another (i.e., the values do not affect one another).

5. Build a k Nearest Neighbors classification model from scratch.

Note: Use Euclidean distance as your closeness metric. You may not use the Scikit-learn library.

This KNN question requires you first to define the metric. In this case, we know it’s Euclidean distance. Then, you would define a helper to calculate the distance between and every data point in our data frame.

6. How would we give each rejected applicant a reason why they got rejected?

What if we had rejected an applicant with a recurring outstanding credit card balance of 10% of their monthly take-home income?

How could we use this data point to help us map towards understanding if this feature was a helpful indicator or not when we have a sample distribution of application outcomes?

7. How would you write a query to get an employee’s current salary?

Due to an ETL error, the employees’ table did an insert instead of updating the salaries when doing compensation adjustments.

And the first step we need to do would be to remove duplicates and retain the current salary for each user.

Given we know there aren’t any duplicate first and last name combinations, we can remove duplicates from the employees’ table by running a GROUP BY on two fields, the first and last name. This allows us to then get a unique combinational value between the two fields.

8. Given N samples from a uniform distribution [0,d], how would you estimate d?

What does a uniform distribution look like? Just a straight line over the range of values from 0 to d, where any value between 0 to d is equally likely to be randomly sampled.

So, let’s make this easy to understand practically. If we’re given N samples and we have to estimate what d is with zero context of statistics and based on intuition, what value would we choose?

9. Why would the same machine learning algorithm generate different success rates using the same dataset?

When they ask us an ambiguous question, we need to gather context and restate it in a way that’s clear for us to answer.

When it says “same dataset”, this could mean the same training dataset or the same testing dataset, or both.

In any of these cases, it could also be asking about the dataset available for the model to use or about the dataset the model uses. These two datasets could be different. For example, different sampling methods over the same available training data could have our model use different data points.

This course is designed to help you with everything you need to know about Machine Learning Algorithms:

Check out these resources from Interview Query to learn more about machine learning scientist interviews:

• Machine Learning Interview Questions guide
• Our modeling and machine learning course
• Facebook Machine Learning Interview Questions