The job search process is crucial for individuals seeking employment, particularly for those without work experience, such as recent graduates. This research aims to examine the behavior of job seekers by investigating whether their profiles align with the skills required in job postings, focusing on data scientists. The study seeks to understand job search patterns and provide recommendations to job seekers on skill acquisition and job fit to improve their chances of securing employment. The research employs an N-gram model to extract job skills from job postings and user profiles, cosine similarity to calculate person-job fit scores, and collaborative filtering methods for skill recommendations. Data is collected from two groups: employed data scientists and students actively seeking data scientist positions. The results indicate that job-seeking students possess skill sets closer to the job market demands compared to employed data scientists. This suggests that recent graduates or those transitioning into new careers may be better prepared to meet job market needs than those already employed in the field. Furthermore, job postings display a more even distribution of similarity to job seekers' skill sets, implying that job seekers have a wider range of opportunities available as their skills align with various job postings. The regression analysis conducted in this study shows that the similarity coefficient was positive but not statistically significant, indicating the potential of similarity as an indicator of employment status change for both job-seekers and employed data scientists. However, the explanatory power of the similarity score appeared to be lower for those actively seeking a job, possibly due to their increased activity in the job market. This research has the potential to guide job seekers in skill development and improve their chances of finding suitable employment.