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The world of technology is advancing at an unprecedented rate, and companies are constantly striving to stay ahead by integrating generative AI or developing their own using open source models and data sets. As a data scientist seeking employment in this age, it is crucial to acquire a wide range of tools and skills to remain competitive in the job market.
In this blog, we will discuss what are the core topics you need to focus on to become an AI data scientist and get hired by your favorite company. We will focus on learning about statistics, data science basics, NLP, rapid engineering, data science portfolio, interview preparation and AIOps. By mastering these basic topics, you’ll be well on your way to becoming a successful AI data scientist and securing his dream job at his favorite company.
Although you can ask GPT-4 to interpret the result, you must understand statistical terminology to reach a conclusion or even ask a question. After the interpretation of the results, you must come up with a plan that is suitable for your company. GPT-4 is not good at finding the correct answer when there are multiple moving parts. This is where our knowledge of statistical analysis will come in handy.
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ChatGPT and GPT-4 are not good at designing customizable plans for your data project. You have to write too many follow-up prompts just to get the action plan right. And even then, you should double check the project plan before presenting it to your manager. All follow-up prompts require an understanding of basic data concepts such as data ingestion, data cleansing, data manipulation, data visualizations, data analysis, and data modeling.
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Even then, there are many things that GPT-4 fails at, such as debugging, research, creating the latest API, and adding specialized code.
Learn more about 20 Data Science Basics for Beginners.
Both text-to-image and text-to-text generation models require expert knowledge of natural language processing. Without it, you won’t be able to fit the model, improve the results, or even find your solution. With the release of ChatGPT, NLP and reinforcement learning have become hot jobs.
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Large language models can be used for text classification, language translation, code generation, question answering, summaries, and more. Without knowledge of NLP, you will not be able to perform text analysis or create AI applications for specific tasks.
The basics of NLP are also necessary for security, understanding of model architecture and data sets. Without it, it will be difficult for you even to pass the initial stage of the interviews.
AI rapid engineering is becoming an increasingly essential skill for all tech workers. Mastering this skill can enable you to write code that is fast and efficient, design comprehensive project plans, troubleshoot effectively, adapt quickly to new technologies, and produce high-quality reports and documentation. The potential applications of this AI are virtually limitless.
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AI rapid engineering helps you communicate better with AI and believe me or not. AI is not here to replace us, but to help us in our workspace. We can write a program or a report in 5 minutes. All you have to do is double check the results.
Check out ChatGPT for the data science cheat sheet, or learn about fast engineering by checking out the best free resources for learning ChatGPT.
Working on portfolio projects and showing your portfolio profile is important. You must have good data science projects on GitHub or DagsHub, Kaggle, and Huggingface. You can even create your website using templates like mine: Abid’s Portfolioor check out my blog on 7 Free Platforms to Build a Strong Data Science Portfolio.
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In today’s digital age, maintaining a strong online presence on LinkedIn has become essential. As evidenced by the job offers I keep getting through LinkedIn and GitHub, actively participating in online discussions and continually working on your portfolio can significantly increase your chances of getting hired. Once you’ve finished your project, it’s important to show your results or create a short tutorial, which you can share on platforms like Medium and KDnuggets. Don’t forget to promote your projects on various social media platforms, as well as tech-focused Discord or Slack groups.
For data science multiple interview sessions, you should prepare for behavioral, situational, statistics, Python code, SQL, NLP, machine learning, and data analysis questions.
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- You can improve your chances of passing the interview stage by working on versatile projects. Check out the complete collection of data science projects: Part 1 and Part 2
- Review mock interviews for each topic. Check out the complete collection of data science interviews: Part 1 and Part 2.
- Review forgotten data science concepts using cheat sheets. Check out the complete collection of data science cheat sheets: Part 1 and Part 2.
- Research the company profile, product category, and employees to understand what they are looking for and try to select your answer accordingly.
- Showcase knowledge of the latest technology and the ability to use AI to improve workflow.
As I mentioned earlier, many companies are looking for data scientists and engineers to integrate AI into their existing products or create entirely new ones. Therefore, it is crucial to be mentally prepared to answer questions related to AI operations.
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For example:
- How would you implement a large language model?
- Do you know how to create, debug, and run data pipelines?
- Do you know how to use docker/kubernetes?
- Do you have experience with Azure, GCP or AWS?
- How would you monitor the models in production?
- How would you update your language model?
These questions are becoming common as companies search for data scientists with knowledge of DevOps or MLOps. You can learn MLOps with this free course.
abid ali awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on creating content and writing technical blogs on machine learning and data science technologies. Abid has a Master’s in Technology Management and a Bachelor’s in Telecommunications Engineering. His vision is to build an AI product using a graphical neural network for students battling mental illness.
