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 varies 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:
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.
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:
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:
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 PhD in machine learning, robotics or computer vision is a preferred qualification.
Many ML scientists make the switch from academia. In fact, almost all FAANG companies hire extensively from PhD 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:
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 average salary by role:
Interviews for machine learning roles tend to dive deep into ML techniques and methodologies. You’ll definitely face ML algorithm questions and Python ML questions, as well as machine learning system design questions and case studies.
Here are some examples of the types of questions you might face in a machine learning interview:
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.
Note: Note that 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.
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.
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 from one another (i.e. the values do not affect one another).
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 dataframe.