Staff Data Science Researcher

Join our companys 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 our companys 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 ev

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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, scal