Senior Data Scientist, Actimize

Job description

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? As a Senior Data Scientist, you will be responsible for designing, developing, and delivering machine learning and Generative AI solutions that enhance fraud detection capabilities. You will work closely with Product, Engineering, and domain experts to translate business challenges into scalable AI-driven products, combining LLMs, classical ML techniques, and domain knowledge into production-grade systems How will you make an impact? • Design and develop end-to-end machine learning solutions – from data collection and preprocessing to model development, evaluation, and deployment. • Build predictive and generative models to extract actionable insights from large and complex datasets. • Apply statistical analysis and quantitative techniques to uncover patterns, trends, and correlations in data. • Utilize statistical techniques and quantitative analysis to identify trends, patterns, and correlations within the data. • Design and build agentic AI systems, including: • multi-step reasoning pipelines • tool-using agents • autonomous workflows for investigation and decision support • Develop RAG-based architectures, integrating LLMs with structured and unstructured data sources. • Translate business problems into analytical solutions in partnership with Product, Engineering, and domain SMEs. • Stay current with advances in Agentic AI, LLM architectures, reasoning systems, and ML research. Have you got what it takes? • Minimum of 4-8 years of hands-on experience in data science and machine learning, with at least 1 year of experience in Generative AI development. • Proficiency in programming languages such as Python , as well as experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn, Hugging Face - Transformers, LangChain etc.). • Strong understanding of machine learning techniques and algorithms, including supervised and unsupervised learning, regression, classification, clustering, and deep learning. • Hands-on experience with: • LLMs and prompt engineering • frameworks like Hugging Face, LangChain, LangGraph / similar • evaluation techniques for generative models • <span style="font-family: 'times ne