About the role
- Work end-to-end: research, prototyping, building production-ready ML/DL/LLM models, and delivering insights that shape the business.
- Identify new product opportunities and unlock value across diverse business domains using data-driven analysis.
- Define requirements, integrate data from multiple sources, and build ML/DL/LLM models that automate and scale business processes.
- Conduct research, run POCs, and deliver production-grade models using MLOps infrastructure.
- Take ownership of initiatives, ensuring solutions are accurate, efficient, and business-impactful.
Requirements
- 4+ years of industry experience — must.
- Proven experience with LLMs and advanced prompt engineering — must.
- Strong skills in gathering, sampling, and wrangling data tailored to business use cases — must.
- Proficiency in production-grade Python with hands-on experience running code in production — must.
- Demonstrated ability to take models from research → POC → production.
- Experience with LLM fine-tuning.
- Familiarity with NLP and traditional ML approaches — advantage.
- Academic degree in a STEM field (Statistics/CS/Math/Engineering) — advantage.
- Background in Cyber, Risk, or Insurance — advantage.
- Excellent communication skills with the ability to translate complex findings into actionable insights.
- Business-driven mindset, motivated by impact and results.
- Competitive salary and equity in a hyper-growth company redefining commercial insurance.
- Strong emphasis on work-life balance: Hybrid model, Wellness days, and more.
- Beautiful offices in Azrieli Sarona, Tel Aviv, steps away from train and bus lines.
- Collaborative, fun, and passionate team that loves solving tough problems together.
ATS Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
ML modelsDL modelsLLM modelsMLOpsPythondata wranglingNLPprompt engineeringmodel fine-tuningproduction-grade models
Soft skills
communication skillsbusiness-driven mindsetownershipdata-driven analysisability to translate findings