Why 90% of Candidates Don’t Get Shortlisted for Interviews

General Guidelines for Creating a Good CV

1. Colour Scheme: Use no more than 2 or 3 colours.
2. Professional Photo: Include a professional photo.
3. Spelling and Grammar: Ensure there are no spelling mistakes.
4. Career Objective: No need to write a career objective at the top.
5. Order of Information: List your experience and education in reverse chronological order.
6. Achievements: Describe past achievements in measurable terms. For example, instead of writing “Worked on building classification model,” write “Delivered classification model which resulted in a cost saving of $10 million per year.”
7. Length: Try to stick to a 1-page format unless it’s impossible to condense the information.

Following these steps will make your CV look polished. Next, focus on the content. Your CV can stand out or get rejected for two main reasons:

• Do you have relevant experience and skills for the role?
• Have you represented these points clearly?

Having worked in the Data Science domain for over 10 years and reviewing 100+ CVs in the last two years, I’ve identified common mistakes and best practices for IT professionals transitioning to data science roles.

Common CV Mistakes of IT Professionals Transitioning to Data Science 

1. Irrelevant Experience: More than 50% of the CV text describes irrelevant experience in detail, often listed at the top.
2. No Summary Section: Lack of a summary section.
3. Irrelevant Projects: Open-source data science projects included are irrelevant to the applied role.

Increasing Your Chances of Getting Shortlisted for a Data Science Role 

1. Summary Section: Include a summary at the top that helps hiring managers understand your overall profile, relevant skills and education/certifications.
2. Relevant Experience: Focus on relevant experience in your CV. Highlight work related to data engineering, SQL, data analysis, software programming (Python, R, C, C++, Java) and stakeholder management. Emphasise the business impact of your work.
3. Industry-Relevant Projects: Include open-source projects with a variety of problem statements relevant to the industry.
4. Education and Certifications: Clearly mention any data science education or certifications in the Education section.

Hiring managers are looking to fill positions as soon as possible with the best candidate. Your goal is to make their job easier by creating a standout CV that compels them to shortlist you for the interview.

Once you get shortlisted, you’re already in the top 10% of candidates for that role.Stay tuned for more insights and tips on landing your dream data science job!