Job Search Strategy for AI and Data Roles
Run a focused job search: target roles, tailor applications, use referrals and handle rejection
A taste of a lesson
I've applied to 150 data analyst jobs over two months and got two screens. What should I change?
That pattern usually points to targeting and the CV rather than your ability. Let's check three things. First, are you applying to roles where you meet the core requirements? Second, does your CV lead with evidence, like projects and results, tailored to each role, or is it one generic version? Third, are any applications coming through people? A referral or conversation often gets a CV read. Could you share the top third of your CV so we can look at it together?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Define target roles and organisations for a focused search
- Tailor CVs and applications to core requirements with real evidence
- Use informational conversations and referrals respectfully
- Track applications, prepare for each stage and handle rejection
Lesson plan
- 1 Choosing your targets Define role types and a list of organisations worth focusing on. Start
- 2 CVs that show evidence Rewrite your CV around impact and the core requirements of target roles. Start
- 3 Conversations and referrals Build relationships through informational conversations and community involvement. Start
- 4 Applying and tracking Tailor applications and run a tracking system with weekly reviews. Start
- 5 Stages, offers and negotiation Prepare for each hiring stage and approach offers thoughtfully. Start
- 6 Resilience and safety Handle rejection, protect wellbeing and avoid scams. Start
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About this tutor
For anyone looking for AI, ML, data or AI product roles, from first jobs to senior moves. We build a search strategy instead of mass applying: choosing target roles and organisations, tailoring CVs and applications to core requirements, informational conversations and referrals, tracking applications, preparing for each stage, negotiating offers in general terms, and staying resilient through rejection. We are honest about competitive markets, variable hiring practices and scams, and we never promise outcomes. Works alongside portfolio and interview preparation.
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About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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