we are looking for a Data Scientist.
This is a high-impact, algorithm-focused Data Science role for a sharp ML practitioner who lives at the intersection of recommendation systems, user psychology, and feedback loops. As our Match Quality & Feedback Loops Expert, you will own the algorithmic core that powers meaningful connections on our platform. You are not here to just build models in isolation; you are here to continuous-tune evaluation metrics, solve cold-start and balance problems, and optimize real-time feedback loops that drive true user satisfaction, engagement, and long-term retention.
What You'll Do:
Algorithmic Match Quality: Design, train, and deploy production ML/AI models for recommendation engines, candidate ranking, and personalization to maximize match quality and user satisfaction.
Feedback Loop Optimization: Build and refine implicit and explicit feedback mechanisms (likes, messaging patterns, churn/retention signals) to continuously evolve algorithmic performance in real time.
Metric Frameworks & Evaluation: Define and implement robust online and offline evaluation frameworks, moving beyond naive accuracy metrics to measure long-term ecosystem health, fairness, and engagement.
Cold-Start & Liquidity Optimization: Develop innovative strategies to address cold-start problems for new users and balance marketplace liquidity across different geographic and demographic cohorts.
Cross-Functional Collaboration: Work hand-in-hand with Product Managers, Data Engineers, and Core AI/ML teams to translate complex behavioral insights into actionable product features.
Requirements: Data Science & ML Veteran: 4+ years of hands-on Data Science experience building recommendation systems, ranking models, or matchmaking algorithms in B2C/tech environments - Must.
Recommendation & AI Expert: Deep theoretical and practical experience with collaborative filtering, graph neural networks (GNNs), reinforcement learning, or modern embedding-based retrieval techniques - Must.
Production & Code Powerhouse: Strong proficiency in Python, SQL, and modern ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn), with experience deploying and monitoring models in production.
Causal Inference & Experimentation: Proven track record of designing A/B tests, measuring causal impact in complex two-sided ecosystems, and handling feedback loop biases.
Fluent Communicator: Native/Fluent English and Hebrew (written and spoken) with the ability to explain complex algorithmic concepts clearly to non-technical stakeholders - Must.
Curious & Impact-Driven: A passionate problem solver who cares deeply about user outcomes and thrives in a fast-paced, iteration-heavy startup culture.
This position is open to all candidates.