Resumes for technology roles tend to be very different from CVs for other roles, and this reflects the entire recruitment process that innovative companies follow.
Instead of simply looking at your qualifications, companies looking for data scientists will take a deep dive into who you are and what you bring to the table in a multitude of unique ways.
The good news is that you can also adapt your job search and make it more dynamic in the name of finding a project that suits your professional goals and aspirations.
1. Define Your Goals Before You Begin Your Research
Sure, everyone in your position has the same end goal: get a job you’re satisfied with. But, drilling down on the specifics you want to have in your next role can give you a lot of clarity during your job hunt.
The best place to start is the job itself. You can be hired as a data scientist, but this is not the only role available in the field.
On the contrary, companies in data science often have teams composed of data engineers, data analysts, data architects, analysts, statisticians, and even data storytellers.
Other job objectives can revolve around wages, time off policy, work environment, type of company, industry, and location.
2. Create an Employer Persona
Now that you have a clearer idea of what you want to achieve, it’s time to determine who you want to work for. This is important because working for a company you like and truly believe in will help you stay motivated, which will produce the best results.
To find the right company, you should create an employer persona. In simple terms, an employer persona is a fictional representation of your ideal employer. Rather than focusing on your immediate superior, think about the type of company that you want to work for. This will help you understand if you’re more suited for B2B, consumer-facing, non-profit, or institutional roles.
3. Keep Up with Market Trends
The entire field of AI is set to grow by 37% by 2030, fueled by the mass adoption that this technology will continue to experience for the foreseeable future. That said, this is far from the only market trend that will impact data science.
Keeping up with market trends is the best way to stay up-to-date with changes that can affect your job hunt and career as a whole. This includes demand for AI and other innovative technologies, compatibility of new tech with legacy systems, and global competition, among many other factors.
4. Polish Your CV
One of the biggest issues with job applications in technology is that most professionals don’t polish up their CVs or resumes before applying. Many choose to simply prepare for a potential interview, but it’s necessary to have a solid CV in order to get past the early stages of the filtering process.
Having a Single Version of Your CV: Custom content is king, whether it’s for public consumption or a job application. As such, you should create a customized CV for each application and do things like place the most relevant experiences atop of the list.
Overloading Your CV with Tech Stuff: There is a baseline of technical knowledge for every job in data science. If you’re confident you’re above it, you should avoid overloading your CV with tech stuff just to prove a point, as it may overwhelm recruiters.
Failing to Implement the Right Formating: Remember the early stages of the filtering process? Nowadays, many companies use AI to filter out CVs using an Applicant Tracking System (ATS) powered by AI, so you should format your CV in a clear way that can be easily read.
5. Collect Recommendations and Build a Portfolio
Next, you should work on your reference game. Think about all the projects you’ve been involved in and the professional relationships you’ve had with your superiors in the past.
Then, create a list of good acquaintances that serve as professional references because they’re solid acquaintances and have experience working with you.
Once you’ve collected three or more references, you can spruce up your portfolio by adding these references and recommendations. Your portfolio should only include projects that you’re allowed to disclose, which may disqualify certain projects if you’ve signed NDAs for them.
6. Choose Fewer Positions and Spend More Time on Each Application
For many professionals, job applications are all about the numbers: send as many as you can and increase your chances of landing something. But, this can easily lead to cutting corners and following other bad habits like copy-pasting introduction letters.
Instead, you should spend more time researching and creating a list of 5 positions you want to apply to. If you have specific places in mind, you can use a VPN to mask your IP and get results that are more relevant to these locations when using search engines.
7. Practice Interview Questions and Up Your Showcase Game
Practicing standard interview questions is always a great way to loosen up and figure out how to deliver your thoughts concisely. These interview questions can vary based on the position, but they can include things like:
Can you walk us through a data science project you’ve worked on from start to finish? What tools, techniques, and models did you use, and why?
How do you handle missing or inconsistent data in a dataset? Can you provide an example from your past work?
How do you explain complex data science concepts to non-technical stakeholders? Can you give an example of when you successfully did this?
At the same time, go through your portfolio and choose projects that showcase your abilities. Only choose projects or segments you’re allowed to disclose. Then, practice presenting and showcasing these projects a few times in case you’re asked about them. If the interview is online, you can even take a few screenshots and use them during your presentation.
8. Stay Tuned to the Latest Advancements and Techniques
Artificial intelligence moves at an alarming speed, so it’s important to stay tuned to the latest advancements in AI and other technology fields. This will help ensure that you’re constantly learning and adopting new skills that broaden your job opportunities in the future.
Here are a few fields you may want to keep a close eye on.
Generative AI: The most iconic type of AI today, this is the category that includes ChatGPT and other platforms that help you create content.
User-Friendly AI: The field that focuses on making AI a normal everyday thing available to everyone on the planet by making it more user-friendly and less complicated to use.
Increased Personalization: As we said earlier, custom is king, to the point that there is a huge chance for this specialty to explode and create a unique industry of its own.
AI Ethics: The field that is responsible for the ethical development, deployment, and application of AI.
What’s Data Science?
Data science is a field that focuses on collecting information, arranging it, and extracting valuable insights from it. The field is a combination of mathematics, computer science, and statistics to analyze information, connect points, and make predictions.
Here are some of the tools used in data science.
Tableau
Apache Spark
TensorFlow
NumPy
Project Jupyter
SAS
Excel
Apache Hadoop
R
Power BI
KNIME
Matplotlib
RapidMiner
Scikit-learn
BigML
PyTorch
D3.js
SQL
MATLAB
Data robot
Data visualization
Can I Use AI to Help Find Better Job Opportunities?
The resounding answer to this question is yes, of course you can use AI to increase your chances of getting a data science role you absolutely love.
Using AI-powered platforms like LinkedIn to conduct your job is a good start, plus resume optimization platforms can help ensure that your CV can be easily digested by readers and ATS.
Ready to Find the Perfect Role?
Data science is a relatively new field that will have an immense impact on healthcare, government services, and a wide range of additional industries. As a data scientist, it’s important to seek out the best possible roles and find creative ways to open up new doors.
One of the best things about data science is that you can apply to roles all around the world. Not only these, but these roles are available in all types of companies across a wide range of verticals, so there’s definitely an opportunity out there that matches your perfect job description.
