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Lead Data Science Researcher
NICE — признанный мировой лидер с огромной базой данных (120 млн взаимодействий ежедневно), что дает уникальное поле для исследований. Гибридный график NiCE-FLEX и работа над передовыми технологиями (Agentic AI) делают вакансию крайне привлекательной для топовых специалистов.
Сложность вакансии
Роль требует редкого сочетания глубоких исследовательских навыков в области LLM и практического опыта создания агентных систем (Agentic AI) промышленного уровня. Высокая планка ожидается как в технической части (архитектура, оценка), так и в лидерских качествах для менторства распределенных команд.
Анализ зарплаты
Предлагаемая позиция Lead уровня в международной компании в Израиле предполагает зарплату выше среднего по рынку. Учитывая специализацию на дефицитном направлении Agentic AI, компенсация может находиться в верхнем дециле для опытных исследователей.
Сопроводительное письмо
I am writing to express my strong interest in the Lead Data Science Researcher position at NICE. With a robust background in developing production-grade AI agents and a deep expertise in LLM orchestration, I am excited about the opportunity to contribute to your Research Group. My experience in architecting multi-agent systems using frameworks like LangGraph, combined with a focus on rigorous evaluation and safety guardrails, aligns perfectly with NICE’s mission to redefine enterprise CX through breakthrough AI.
Throughout my career, I have demonstrated a track record of translating complex research into reliable, scalable capabilities. I am particularly drawn to this role because of NICE's commitment to challenging limits and its focus on multimodal and agentic AI. I am confident that my technical leadership and hands-on experience with AWS Bedrock and modern ML ecosystems will allow me to make a significant impact on your roadmap and mentor the next generation of researchers within your team.
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Присоединяйтесь к лидеру рынка CX и создавайте будущее агентного ИИ вместе с экспертами NICE!
Описание вакансии
At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.
So, what’s the role all about?
Join NICE’s Research Group and shape the next generation of AI for enterprise CX. As a Lead Data Science Researcher, you will own highimpact research initiatives across NLP, Vision, and multimodal domains, with a strong emphasis on large language models (LLMs) and agenticAI systems.
You will combine deep handson technical work with leadership—setting direction, mentoring peers, and translating breakthrough ideas into reliable, productiongrade capabilities for NICE’s contact center solutions.
You will collaborate closely with researchers, engineers, product leaders, and subjectmatter experts to define strategy, validate research hypotheses, and lead the transition from experimental agentic systems to reliable, realworld deployments.
How will you make an impact?
- Lead end‑to‑end research initiatives across NLP, Vision, and multimodal modeling, with a strong focus on LLM‑based and agentic‑AI systems.
- Architect, prototype, and evaluate singleagent and multiagent systems, including planning, tool use, memory, and orchestration.
- Establish and own best practices for safe, controllable, and scalable AI agents, including evaluation frameworks, guardrails, fallback strategies, and observability.
- Act as a technical authority on LLM and agentic systems, guiding architectural decisions, evaluation strategies, and engineering tradeoffs.
- Define rigorous offline and online evaluation strategies (KPIs, A/B testing, cost/performance tradeoffs) grounded in realworld constraints.
- Deliver select research components at production quality and partner closely with productization teams to harden and deploy endtoend solutions.
- Mentor researchers and data scientists, raising the bar for technical rigor, engineering quality, and applied research impact.
- Communicate complex findings and risks clearly to crossfunctional stakeholders and leadership.
- Stay at the forefront of AI research, contributing to the team’s agentic‑AI roadmap and long‑term research vision and help set teamwide standards and guidelines.
Have you got what it takes?
- Skills-first profile with proven, hands‑on impact in applied AI. (Formal degrees welcome but not required.)
- Strong Demonstrated experience building production‑grade AI agents that perform multi‑step reasoning and tool‑based actions (e.g., tool invocation, planning, memory).
- Mandatory: Experience with agent frameworks/orchestration layers or custom agent runtimes (e.g., LangGraph/LangChain, semantic routers, workflow engines, or in‑house frameworks).
- Strong practical expertise with LLMs (AWS Bedrock or similar platforms), including evaluation, prompt/program design, and safety patterns.
- Strategic problem-solving leadership: You proactively shape ambiguous business questions into well-defined analytical goals, challenge underlying assumptions, and ensure the work is focused on the problems with the highest impact.
- Bar‑setting analytical rigor—applied pragmatically:You anticipate bias, confounders, and risks of misinterpretation early, apply the right level of methodological rigor for the decision at hand, and help others distinguish between “theoretically perfect” and “fit for purpose.”
- Efficient, scalable thinking:You balance depth with speed, favor simple and robust solutions over unnecessary complexity, turn one‑off analyses into reusable insights, and help the organization avoid reinventing or over‑engineering solutions.
- Track record of translating research into reliable systems in partnership with engineering/product teams.
- Proficiency in Python and modern ML/DL libraries; strong AI engineering skills (clean architecture, testing, CI/CD, observability), as well as familiarity with HuggingFace or similar model ecosystems and open‑source tooling.
- Experience applying GenAI to DS workflows (LLM‑as‑a‑judge, synthetic data generation, weak labeling, automated eval).
- Excellent communication and mentoring skills; fluency in English.
You will have an advantage if you also have:
- Experience with multi‑agent systems, reinforcement learning, or policy optimization for agent control.
- Expertise in multimodal or vision‑based deep learning.
- Operating experience with cloud ML at scale (AWS or equivalent).
- Leading/mentoring globally distributed teams.
What’s in it for you?
Join an ever-growing, market-disrupting, global company where the teams – comprised of the best of the best – work in a fast-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!
Enjoy NiCE-FLEX!
At NiCE, we work according to the NiCE-FLEX hybrid model, which enables maximum flexibility: 2 days working from the office and 3 days of remote work, each week. Naturally, office days focus on face-to-face meetings, where teamwork and collaborative thinking generate innovation, new ideas, and a vibrant, interactive atmosphere.
#LI-Hybrid
Requisition ID: 10398
Reporting into: Group Lead, Data Science, CX
Role Type: Individual Contributor
*About NiCE*
NICE Ltd. (NASDAQ: NICE) software products are used by 25,000+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3+ billion financial transactions.
Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30+ countries.
NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.
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Навыки
- Python
- Large Language Models
- NLP
- Computer Vision
- LangChain
- AWS Bedrock
- Hugging Face
- Generative AI
- Reinforcement Learning
- CI/CD
- PyTorch
- TensorFlow
Возможные вопросы на собеседовании
Проверка практического опыта работы с фреймворками, указанными в вакансии.
Расскажите о наиболее сложной архитектуре ИИ-агента, которую вы проектировали. С какими проблемами в планировании и использовании инструментов (tool use) вы столкнулись?
Вакансия делает упор на надежность и безопасность систем.
Какие стратегии оценки (evaluation frameworks) и защитные барьеры (guardrails) вы внедряли для предотвращения галлюцинаций и обеспечения предсказуемости поведения LLM-агентов?
Оценка способности кандидата работать с неопределенностью и бизнес-целями.
Как вы подходите к приоритизации исследовательских задач, когда бизнес-требования амбициозны, а технологические возможности LLM постоянно меняются?
Проверка навыков менторства и технического лидерства.
Опишите случай, когда вам нужно было повысить уровень технической строгости (analytical rigor) в команде. Как вы этого добились?
Проверка знаний в области мультимодальности, упомянутой в описании.
Каков ваш опыт интеграции Vision-моделей в агентные системы? В чем основные сложности синхронизации контекста между разными модальностями?
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