Data Scientist
AI in this role
At JFrog, we’re reinventing DevSecOps to help the world’s greatest companies innovate. Our team of industry-leading software security experts is a true pioneer, constantly pushing the boundaries with original research and technological innovation. JFrog is a special place with a unique combination of brilliance, spirit, and just all-around great people. Thousands of customers, including the majority of the Fortune 100, trust JFrog to manage, accelerate, and secure their software delivery from code to production – a concept we call “liquid software”. Wouldn't it be amazing if you could join us on our journey?
We're looking for an experienced Data Scientist to join our DS team under the CIO office. The key responsibilities are to provide our internal business partners with robust, reliable, and innovative AI / ML / DS solutions for their business problems, providing direct value to the business. An ideal candidate blends solid data science foundations, proven backend engineering skills, and a demonstrated track record building real-world systems. You should be able to take an idea (or propose one) and independently work from POC to production MVP, know when to move fast and when to slow down, while continuously communicating with stakeholders as their trusted partner, and enjoy collaborating with members of the team.
As a Data Scientist at JFrog you will...- Listen to our business stakeholders and their challenges and propose data-driven solutions to problems using ML models and advanced statistical methods. For example, a classifier for a rare event that will trigger business action, or a time series predictor in a noisy environment that will affect business planning.
- Work side-by-side with business units to gather requirements, collaborate with cross-functional teams, iterate on the process of data research, and deliver to production.
- Own ML models full lifecycle and monitor existing models, while continuously improving our automation pipeline from a DS perspective.
- Rapidly prototype new ideas (e.g., custom RAG-based solutions, Agentic processes, knowledge graphs) based on applied research or academic concepts, and if successful, productionize.
- Be a primary contributor for the semantic engine component of the organizational brain platform and be able to serve as a technical authority in that domain.
- Look for improvement and innovation opportunities with business impact, and be a source of high-quality ideas.
- Be an independent contributor with excellent communication skills, both in writing and verbally.
- MSc. in computer science or equivalent quantitative field (e.g., Mathematics, Statistics, Engineering, Economics), with a focus on Machine Learning.
- At least 5 years of proven industry experience in DS/ML roles, with a proven record of developing ML models from initiation to production.
- Strong foundations in data science and machine learning, including how to evaluate model outputs & performances.
- Ability to think scientifically and investigate problems using messy, ambiguous, or incomplete data and communicate results clearly and efficiently.
- Proficient hands-on experience with the Python ML/DL ecosystem.
- Hands-on experience in implementing and evaluating custom RAG systems with a deep understanding of how they work, how to evaluate their performance, and how to improve them.
- Ability to serve as a technical expert for RAG and complex agentic workflows such as deep research and wide search (almost complete recall, high precision, high scale, low latency, low cost).
- Proficient hands-on experience with databases.
- Experience in Time-Series forecasting.
- Proven ability to work independently; delivery-oriented.
- Team player with effective interpersonal skills. Manage relationships with key partners across different functions. Able to collaborate with remote teams effectively.
- Excellent spoken and written English.
How we rate this
Data Scientist at JFrog rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG and AI Agents. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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