Data Scientist

Description We're looking for a Data Scientist to join our fast-growing Data Science team focused on AI-first features. This role sits at the core of how Lusha personalizes the sales experience, designing and deploying end-to-end ML models that move real business metrics. You'll collaborate closely with ML engineers, product managers, and data engineers, and join weekly sessions where the team shares research, tests new ideas, and learns together. Hands-on experience using AI development tools such as Cursor or Claude Code is a must for this role. At Lusha, we're building for builders. We build fast and AI-first, so we look for builders. By a builder, we mean someone who turns "maybe" into "done". This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office. This might be for you if: You enjoy solving complex business problems with both classic and SOTA ML techniques You are eager to build robust and efficient agentic pipelines that deliver unique value and experience for our users You like working with business stakeholders to define and prioritize problems solved through DS/ML pipelines You care about building highly personalized, data-driven experiences that directly move engagement, revenue, and retention You enjoy owning your solutions end to end, from success criteria to monitoring and maintenance You actively leverage AI tools to increase productivity and accelerate development Requirements 3-5 years of hands-on Data Science experience, including developing ML systems MSc or PhD in a relevant quantitative field (e.g., Computer Science, Statistics, Applied Math, Physics, Engineering) with a thesis Proficiency with Python (Spark, Pandas, NumPy, SciPy, scikit-learn) Strong technical aptitude with the ability to quickly learn and adapt to new frameworks, tools, and systems Proven experience with tabular data, time series analysis, and building robust, scalable ML pipelines Experience with text-based data and NLP - Must Experience with agentic flow development - Must Proven track record of deploying end-to-end ML models into production - Must Nice to have: Experience training models for adaptive or individualized user experiences Experience with Big Data technologies - Spark, Databricks