Camille, 24: "I landed my first Data Scientist role 4 months after graduating"
Camille Dubois, 24, graduated with a Master's in Data Science in July 2025. First permanent role signed at BNP Paribas in November 2025. Here's how she turned a "junior, no real experience" profile into an application that actually got through the filters.
At a glance
| Metric | Before Traject (6 weeks) | With Traject (10 weeks) |
|---|---|---|
| Target role | Data Scientist | Data Scientist |
| Pro experience | 2 internships (10 months total) | 2 internships (10 months total) |
| Applications sent | 42 | 45 |
| Replies | 3 (7%) | 14 (31%) |
| Interviews | 2 | 7 |
| Offers received | 0 | 2 |
| Signed salary | — | €46k + variable |
The starting point: doing everything right, getting nothing back
Camille graduated with distinction from a Master's in Data Science, two internships at SaaS vendors and a Kaggle side-project ranked in the top 15%. On paper, the profile recruiters say they want. In practice, something else entirely.
For six weeks, she sent 42 applications via LinkedIn and corporate career sites. Three replies. Two HR interviews. No follow-up.
"I was doing what everyone told me to do. A clean CV in Canva, a tailored cover letter per ad, a broad search. Except my CV literally wasn't making it through ATS filters — and I had no idea."
Three underlying blockers
- The CV didn't pass ATS filters. Custom fonts, columns, a PDF exported as image. The automated scanner picked up half the skills, at best.
- The positioning was too broad. "Data Scientist" without specifying: data engineering? applied ML? research? Camille was applying to very different roles with the same dossier.
- Impostor syndrome was killing the interviews. Facing hyper-credentialed LinkedIn profiles, Camille was minimizing her own track record live in interviews — to the point one recruiter told her "I'm not sure you even believe your own application."
The shift: objectify the market instead of applying on gut feeling
On a former classmate's recommendation, Camille switched to Traject in early September. The first step wasn't producing a new CV, but mapping what companies hiring junior Data Scientists in France actually want.
The diagnostic surfaced three things she didn't know:
- 78% of "junior Data Scientist" job ads required advanced SQL — which she wasn't highlighting.
- "Applied ML" roles (the best fit for her) represent 41% of the market — far more than the pure R&D positions she was targeting.
- Her starting Employability score: 62/100. Gaps identified: advanced SQL, cloud exposure (AWS or GCP), and an internship narrative that read too academic.
"For the first time I had real numbers. Not a coach telling me 'you need to improve' — a concrete read of what the market actually wants, what I don't have, and which roles are closest to my real profile."
What concretely changed
1. ATS-friendly CV per ad, generated in 2 minutes
Instead of one CV manually tweaked, Camille generates one version per ad. The generator rephrases bullets based on keywords detected in the posting, without inventing anything: the experiences stay hers, only the angle shifts.
Immediate effect: her response rate jumped from 7% to 31% over the first six weeks with Traject.
2. A focused 4-week learning plan
The generated plan flagged 3 absolute priorities: advanced SQL (window functions, CTEs), AWS basics (S3, Lambda, Redshift), and how to talk about her projects using the STAR method. Not 30 scattered Udemy courses. Three topics, validated by a readiness score that climbed with each sub-module completed.
"Before, I jumped on anything that popped up on LinkedIn Learning. With Traject, I had a three-line plan for a month and stuck with it. For the first time, I watched my readiness score go from 62 to 81 in 4 weeks."
3. Role-specific interview prep
For each job where she got a callback, Camille pulled the targeted question bank for the role and seniority. She prepped STAR answers to 8 key questions and walked into interviews with a coherent narrative.
4. Centralized pipeline and follow-ups
No more Excel. Every application in a Kanban view (sent, follow-up, interview, offer, rejected). Follow-ups auto-triggered at D+7 if no reply, D+3 after an interview. Across 45 applications, zero forgotten follow-ups.
Results at 10 weeks
- 2 concrete offers: a Data Scientist permanent contract at BNP Paribas (€46k + variable) and a permanent contract at a FinTech scale-up (€44k). Chose BNP for the stability and mentorship program.
- Salary negotiated +€3k over the initial proposal, leveraging Traject's market data (junior Data Scientist Paris median: €44k).
- Final Employability score: 81/100, vs 62 at the start.
- Time saved on admin: roughly 6 hours per week thanks to the centralized pipeline and auto follow-ups.
What Camille would tell other fresh grads struggling out there
"The market isn't closed to juniors, it's just badly readable when you're fresh out of school. The problem isn't your level, it's that no one taught you to read what recruiters actually want. Once you have the right numbers in front of you — which skills, which keywords, which roles actually hire — everything becomes manageable."
Camille has been at BNP Paribas for three months. She keeps updating her Traject profile and tracking her AI Resilience score, on a team where half the projects touch LLMs.