Data Science Team Leader

ONE datAI, a division of One Technologies, is a premier provider of professional Data and AI solutions. Since its establishment in 2006, the company has been at the forefront of Israel׳s data innovation field, offering services in data infrastructure, analytics, AI/ML solutions, and more. With a team of over 200 skilled professionals, ONE datAI specializes in transforming complex data challenges into strategic opportunities. Trusted by leading organizations across divere sectors such as finance, healthcare, retail, transportation, and manufacturing, ONE datAI is committed to delivering excellence and driving innovation.

About the Role 

We are looking for an experienced Data Scientist Team Leader to lead and grow our data science team while delivering high-impact data science solutions for our clients. The role combines hands-on technical work with team leadership, focused on solving real business problems using classical machine learning techniques — building predictive models, uncovering patterns in data, and turning analytical insights into production-ready systems. 

The Team Leader works closely with clients and internal engineering teams to understand business challenges, design appropriate modeling approaches, and deliver solutions that drive measurable value, while mentoring team members and owning the technical quality of the team's work. Candidates with additional experience in Generative AI and LLM-based systems will have a strong advantage, as we increasingly combine classical ML with modern GenAI capabilities. 

Key Responsibilities 

• Team Leadership and Management  
• Lead, mentor, and grow a team of data scientists, supporting their technical and professional development 
• Plan and prioritize the team's work across multiple client projects and delivery timelines 
• Set technical standards and review team members' modeling approaches and deliverables 
• Own the end-to-end delivery, quality, and success of the team's data science solutions 
• Modeling and Problem Solving  
• Design and build machine learning models to address client business problems 
• Translate business questions into well-defined data science tasks 
• Perform exploratory data analysis, feature engineering, and model evaluation 
• Select appropriate algorithms based on data characteristics and business constraints 
• Classical Machine Learning Delivery  
• Develop supervised learning models using techniques such as XGBoost, Random Forest, gradient boosting, and logistic/linear regression 
• Apply unsupervised learning methods including clustering, dimensionality reduction, and anomaly detection 
• Build robust evaluation pipelines with proper validation strategies and performance metrics 
• Ensure models are interpretable and aligned with business needs 
• Production and Collaboration  
• Work with data engineers to move models from notebooks to production environments 
• Monitor model performance and support retraining and maintenance cycles 
• Document modeling decisions, assumptions, and results clearly 
• Collaborate with client teams to ensure successful adoption of delivered solutions 
• Stakeholder Communication  
• Present analytical findings and model results to both technical and non-technical audiences 
• Work directly with clients to refine requirements and align on success criteria 
• Translate complex modeling concepts into clear business language 

Experience 

• Several years of hands-on experience as a Data Scientist 
• Proven track record of building and delivering ML models that reached production 
• Experience working directly with business stakeholders and translating their needs into data science solutions 
• Experience leading or managing a team of data scientists or analysts and owning their delivery 

Required Expertise 

• Strong command of classical machine learning algorithms — XGBoost, Random Forest, gradient boosting, regression methods, and clustering techniques 
• Solid understanding of statistics, probability, and core ML theory 
• Proficiency in Python and the standard data science stack (pandas, scikit-learn, NumPy) 
• Experience with feature engineering, model validation, and handling real-world messy data 
• Familiarity with SQL and working with structured data at scale 
• Experience deploying or supporting models in production environments 
• Proven experience leading, mentoring, or managing a team of data scientists or analysts 

Advantage 

• Hands-on experience with Generative AI and large language models 
• Familiarity with RAG architectures, vector databases, and embeddings 
• Experience with LLM orchestration frameworks such as LangChain or LlamaIndex 
• Experience combining classical ML approaches with GenAI solutions