I recently wrote an article about how to write a data science resume for beginners that was received well by the community. I decided to extend that article for those who are more experienced but still need to polish their resume and skills to get into senior data science roles. This may help some of you find answers to questions like “How I should present myself for potential employers as a senior data scientist” or “What skills should I gain to climb up the ladder of data science in a corporate?” Note that whether you have the required skills you must tailor your resume to present yourself as skillful as you are. You must describe your skills in the resume under three major skills: software architecture, deep learning, and big data. You can read the “beginners” article below.
— Software Architecture
If you want to take interviews for senior roles in data science, you must know, in full, the standard practices of software development. I briefly explained them in the previous article. Beyond that, you must know how to design a scalable architecture for a data science project that can be easily understood and maintained. In that case, everyone can contribute to the project and help you keep it up and running. Now, the question is how to show potential employers that you have those skills.
A simple, not the best, way is to emphasize on your resume that you know various design patterns and software architectures, especially for data science projects. Knowing different design patterns helps you write more efficient queries while knowing various software architecture helps you build a scalable architecture. As an example, if you develop a data science project using the layered pattern, you can remove unnecessary dependencies and let various teams work on the projects.
As an AI director interviewing candidates I would like to see these skills in action though. So I recommend developing your sample project, the one that you host on Github or Bitbucket, using these techniques. When I review a codebase, I can easily identify a well-thought architecture. I am certain that your potential employer will be able to do the same. Looking into candidates' Github accounts becomes a standard procedure in reviewing their resumes.
— Deep Learning
At this time, you must be aware of the significance of knowing how to develop and maintain an ML pipeline. However, as a candidate for senior roles, you must know advanced machine learning techniques such as deep learning. You must be a master of the best performing deep learning architectures that are relevant to the job position you applied for. For example, if you want to work in natural language processing you must definitely know BERT or GPT3. You can read more about BERT in the article below.
Plus, your resume must show that you had hands-on experience developing solutions using deep learning. For example, it must show that you know answers to questions such as:
How to manage the convergence mechanism in the training process?
How to apply transfer learning on a pre-trained network?
How to minimize redundant computation?
How to reduce the sensitivity of a deep learning technique?
Note that although deep learning is a subset of machine learning, its nuts and bolts are different.
— Big Data
The traditional data processing techniques fail on extremely large data sets, a.k.a, big data. For example, when you need to load data in the computer memory, you are bounded to the size of the memory. These days, since most datasets easily exceed that limit, the distributed data processing technologies such as Apache Spark™ have become popular. They are also helpful to handle real-time data streaming where a single computer can not respond accordingly.
You, as a senior data scientist, must have hands-on experience with these technologies and explicitly mention that in your resume. For example, one can write this: “Developed an ML pipeline using Spark ML and PySpark” or “Built an efficient data processing pipeline using Spark and Databricks”. These statements briefly show your exposure to Spark technology as well as Databricks, a unified cloud-based platform to store and process big data.
Many technologies were introduced in recent years; nevertheless, you just need to know the one that is currently used. So, I recommend not filling out your resume with the list of various big data technologies. It would be better to choose one, and explain what you have done with that.
The Last Words
To be able to take an interview for a senior data scientist position, you must certainly show the following skills in your resume:
How to create a scalable and easy-to-maintain software architecture?
How to implement advanced algorithms, e.g., deep learning?
How to build an efficient pipeline to store and process big data?
Please note that nothing is guaranteed but you will increase your chances by taking these notes in your mind.
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