דרושים » ניהול ביניים » AI Research Team Lead

We are looking for a talented AI Applied Researcher to help us develop advanced AI solutions that will empower impactful projects. The Data Science team leads the AI research and innovation efforts of the company, and is focused on pushing AI boundaries to enhance the product and optimize processes.

What Youll Do

Lead an applied research for AI-driven features in the platform
Research and optimization agentic flows to solve complex problems
Collaborate with engineering teams to design, build, and maintain production pipelines
Conduct experiments and evaluate the performance of AI models, algorithms, and techniques
Stay up to date with the latest developments in AI, machine learning, and related fields, focusing on LLMs, exploring how emerging technologies can be applied to improve products and services.

We are seeking an experienced and visionary Data Science Team Lead to join our dynamic technology organization. The successful candidate will lead a team of talented data scientists, driving innovation and delivering business value through advanced machine learning techniques and Generative AI solutions. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge data science methodologies, exceptional leadership skills, and a passion for continuous learning and development.
Responsibilities
Lead, mentor, and develop a team of data scientists both methodologically and technically to deliver high-impact projects aligned with our business objectives.
Design, implement, and optimize classic machine learning models to solve complex business problems.
Apply gradient boosting techniques (e.g., XGBoost, LightGBM, CatBoost) to enhance predictive accuracy and model robustness.
Drive the exploration and integration of Generative AI applications, including Large Language Models (LLMs) to create innovative solutions for our products and services.
Collaborate with cross-functional teams (engineering, product, business) to translate business requirements into actionable data science projects.
Establish best practices for model development, validation, deployment, and monitoring in production environments.
Promote a data-driven culture, encouraging experimentation, sharing of knowledge, and adoption of state-of-the-art technologies.
Communicate project progress, insights, and results to stakeholders at all levels of the organization.

Required Data Science Manager
As a Data Science Manager, you will help shape the future of the experiences we build for billions of people and hundreds of millions of businesses, creators, and partners around the world. You will apply your people leadership, project management, analytical, and technical skills, creativity, and product intuition to one of the largest data sets in the world. You will collaborate on a wide array of product and business problems with a wide-range of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will influence product strategy and investment decisions with data, be focused on impact, and lead and grow a impact oriented team. By joining us, you will become part of the analytics community dedicated to skill development and career growth in analytics and beyond. About the role: Product leadership: You will use data to understand the product and business ecosystem, quantify new opportunities, identify upcoming challenges, and shape product development to bring value to people, businesses, and us. You will help develop strategy and support leadership in prioritizing what to build and setting goals for execution. Analytics: You will guide product teams using data and insights. You will focus on developing hypotheses and employ a varied toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches to test them. Communication and influence: You wont simply present data, but tell data-driven stories. You will convince and influence leaders using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner. People leadership: You will inspire, lead and grow a team of data scientists and data science leaders.
Data Science Manager Responsibilities
Lead a team of data scientists to develop strategies for our products that serve billions of people and hundreds of millions of businesses, creators, and partners around the world
Drive analytics projects end-to-end in partnership with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions
Influence product direction through clear and compelling presentations to leadership
Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches
Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends
Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations
Contribute towards advancing the Data Science discipline, including but not limited to driving data best practices (e.g. analysis, goaling, experimentation), improving analytical processes, scaling knowledge and tools, and mentoring other data scientists.

We are seeking an experienced and visionary Head of Research to lead, shape, and advance the future of AI innovation across the organization. In this role, you will own the research strategy end-to-end - from long-term roadmap design to hands-on exploration, and guide a multidisciplinary team of NLP research engineers and linguists toward breakthroughs that elevate our core medical coding engine.
You will play a key role in the company, balancing exploratory research with high-impact product delivery. You will partner with Product, Engineering, and executive stakeholders to identify strategic opportunities, drive prioritization, and translate research capabilities into real-world outcomes at scale.
Responsibilities:
Manage and mentor a multidisciplinary research team while driving both technical excellence and personal growth.
Define the long-term research roadmap aligned with product and business objectives.
Lead the full research lifecycle, from ideation and feasibility studies to experimentation, evaluation methods, and production-ready delivery.
Evaluate and introduce new research capabilities and emerging trends such as LLM-based reasoning, semantic/graph modeling, hybrid rule-learning, and other frontier techniques.
Continuously track scientific trends to maintain a state-of-the-art research practice.
Collaborate closely with Product and Engineering teams to translate domain challenges into algorithmic solutions with measurable product impact.
Represent the Research function at leadership forums, communicate priorities and outcomes, and align stakeholders around research strategies and milestones.

We are looking for a Data Science Team Lead to lead and grow a talented team working on our core data science challenges.
This is a people-first leadership role. Your primary responsibility will be to support, coach, and develop a team of data scientists and an ML engineer, ensuring their well-being, growth, and long-term success. You will set high technical standards, guide problem definition, and help the team consistently choose the right solution for each problem-whether that involves LLMs, classical ML, or simpler approaches.
Youll operate in a fast-paced startup environment, working closely with engineering, research, product, data, and legal stakeholders. While the role includes some hands-on technical involvement, success is measured by team health, decision quality, and sustainable delivery-not personal output.
Responsibilities:
Lead, mentor, and coach a team of data scientists and an ML engineer
Conduct regular 1:1s and support career development and well-being
Own technical direction and ensure high-quality ML decision-making
Guide problem definition and solution selection (LLMs vs classical ML)
Establish and maintain ML development processes (reviews, gates, evaluations)
Balance short-term delivery with long-term model quality
Communicate risks early and push back on stakeholders when needed
Take full ownership of team execution within 2-3 months.