Data science skills needed to land a great job

Hi, I’m Olga! I have years of experience in data science, most recently at eBay. Now, I work as an industry mentor at Pathrise, helping data scientists land a great role through technical workshops and 1-on-1s. Check out my article on data science skills needed to land a great job.

Data science is a highly skilled field. It usually requires a degree and sometimes even graduate education to find a job. Recruiters are looking to make sure that new employees will provide impact and be an asset for the team. We have worked with many data scientists to land their first role. That means we know what data science skills are needed to be successful and get a data science job.

Statistics and probability

So much of data science is probability and statistics. In fact, many aspiring data scientists major in statistics or study it extensively before entering the workforce. Companies look to the data scientists on their team to use processes, algorithms, and systems to extract knowledge, determine insights, and make informed decisions from data and metrics. Specifically, data scientists are looking to explore data, identify relationships and dependencies, predict future trends, discover patterns, and uncover anomalies. Those in this role should also be able to communicate their findings to less technical team members. In addition, they need to make recommendations for actions.

Multivariable calculus and linear algebra

A strong background in linear algebra and multivariable calculus is very important if you are looking for roles at companies that are data-centered. On your resume and in your portfolio, highlight the classes you have taken in these subjects. Plus, showcase the projects that made use of these concepts. At some point in your data science career, you will likely be asked to create your own implementations and models. Make sure you are proficient in:

  • Derivatives
  • Gradients
  • Plotting
  • Finding min and max values of Cost, Step, Sigmoid, Logit, ReLU, scalar, vector, matrix, and tensor functions.

Collecting, sorting, and visualizing data

One of the main responsibilities of a data scientist is collecting, sorting, and visualizing data. The data can be gathered from external assessments, like user tests or A/B tests, or internally, from analytics gathered by the data scientist or another team member. Once they have their dataset ready, data scientists need to clean the data. This involves making sure the variables are consistent, verifying that the data is accurate, and removing any mistakes or obvious outliers. Then, data scientists use visualization tools like Tableau to create a story of the data and explain the next steps for the team and company.

Machine learning

Having a broad understanding of machine learning and being able to cite that on your resume can be helpful for an aspiring data scientist applying for a role at a data-driven company like Netflix, Uber, or Google. These types of companies have so much data at their disposal. This means machine learning is frequently the only way to accomplish the goals in a reasonable timeframe. Luckily, there are a fair number of tools that data scientists can use, like TenserFlow, BigML, and DataRobot, to make their machine learning tasks somewhat easier.


Software engineering and data science go hand-in-hand. This means that knowledge of programming is essential to success in a data role. Statistical programming languages like Python and database querying languages like SQL are absolutely necessary for a data scientist to have on their resume. Most data scientists also know R and some amounts of Java and C++. These languages depend on the types of problems they typically work on or the types of companies they are interested in.

Pathrise is a career accelerator that works with people 1-on-1 so they can land their dream job in tech. If you want to work with any of our mentors 1-on-1 to help you land a great job in data science, become a Pathrise fellow.

Apply today.

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