About the Role
As a Senior Data Science Researcher in our Workforce Management group, you will be part of a cross-sectional team working at the intersection of machine learning, GenAI, agentic AI, ML-Ops and Operations Research. You will develop the next generation of NICE’s Workforce Management solution for our customers’ contact centers - forecasting work volume, scheduling agent shifts, managing time-off requests, and owning the full lifecycle of the work schedule, in a high-stakes production environment serving many customers. Our solution has been recognized as the market leader for over 10 years.
How will you make an impact?
- Developing end-to-end ML systems, tailored to specific project needs, to train and serve production-level models in a high-stakes environment.
- Deploying GenAI and agentic AI systems to customers in production, improving on existing solutions or creating new services from scratch.
- Designing and building agentic AI flows - tool use, multi-step reasoning, and orchestration.
- Forecasting work volume and optimizing shift schedules using time-series methods and mathematical optimization.
- Integrating new features and solutions into large existing software systems, stringing multiple disciplines together.
- Running unstructured evaluation processes - finding noteworthy phenomena, experimenting, and feeding conclusions back to the team.
- Owning projects end-to-end, leading team members and colleagues to efficient planning and delivery.
Have you got what it takes?
- 5+ years of experience as an applied researcher or research engineer.
- Loves reading and writing code.
- Hands-on Python developer - training models, using trained models, data carpentry, preprocessing and analysis.
- Hands-on with PyTorch.
- ML-Ops and cloud experience - deploying and serving models on AWS or another cloud provider.
- Experience with mathematical optimization (e.g. Gurobi).
- Hands-on experience designing and building agentic AI flows.
- Effective Claude user.
- Independent and analytical thinker - capable of running unstructured evaluations and driving conclusions.
- Experience working in an agile environment.
You will have an advantage if you also have:
- MSc in Computer Science or an equivalent engineering field.
- Experience with HuggingFace, scikit-learn, and pandas/SQL.
- Containerization with Docker.
- LLM-specific experience: RAG, fine-tuning, evaluation frameworks.