SITUATION

A PhD in physics and mathematics reached out for support in transitioning to a career where he could apply his expertise in math, programming, and physics. Despite submitting numerous applications for data scientist and analyst roles, he had been unsuccessful. Upon reviewing his applications, it became evident that his CV was too academic, failing to clearly showcase the value he could bring to a company or articulate his future career direction and motivations.

Although he had explored the labor market extensively and identified data science as a promising career path, his applications were not yielding results, leaving him frustrated and unsure of his next steps.

STRATEGY + RESULTS

The first step was to evaluate whether he truly understood the roles he was applying for and whether they matched his background. Given his PhD-level expertise, he risked being both overqualified for some roles and underqualified for others, particularly in technical skills like deep learning and machine learning. A thorough screening of job ads revealed that many of his applications were for roles that did not align with his skill set.

To refine his approach, we focused on connecting with industry professionals to better understand the skill expectations at different job levels. With this clarity, he began tailoring his applications, which resulted in his first interview invitations. However, during the interview process, he realized that he needed to strengthen his programming, deep learning, and machine learning skills to become more competitive.

Feeling confident in his chosen career path, he made a strategic decision to invest in a six-month course to bridge his skill gaps. While still studying, he received a job offer for a Data Scientist position, successfully transitioning into the industry and securing a future aligned with both his technical expertise and career aspirations.