We are a leading innovator in the financial compliance space, serving some of the worlds largest banks and financial institutions. As a Senior Data Scientist, youll be at the forefront of this critical mission – to help these organizations meet regulatory obligations by monitoring vast volumes of employee communications.
Our team is responsible for developing advanced NLP\LLM based solutions that can detect rare and subtle indicators of suspicious behavior hidden within massive streams of unstructured text. This is a high-impact role that requires balancing accuracy, scalability, and cost-efficiency in a production environment.
What Youll Do:
Lead data science project E2E: Research, experiment, develop and productize advanced NLP, LLM and autonomous agent solutions in various domains such as classification, summarization, translation, information retrieval and AI assistant.
Use advanced deep learning and large language models, to enhance our ability to find the needle in a haystack in the world of structured & unstructured data of financial electronic communication.
Conduct rigorous experimentation, analysis, and optimization to ensure performance, efficiency, and scalability.
Develop processes, tools and frameworks to monitor and analyze model performance and data accuracy.
Innovate, develop, and test state-of-the-art approaches to solve our challenging problems: domain adaptation, fine-tuning, prompt engineering, zero- and few-shot learning, autonomous agents, using open and close large language models.
Assist and mentor other team members in the implementation and research.
Collaborate closely with domain experts, product and engineering teams to translate research breakthroughs into scalable commercial products.
Identify the latest technology to improve our AI capabilities in a variety of languages and help shaping our AI roadmap.
M.Sc. in Computer Science or a related field. Ph.D. – an advantage.
5+ years of hands-on industry experience in NLP, deep learning, large language models, generative AI, and prompt engineering.
Proficiency in Python – a must.
Proven track record of designing and operating AI/ML systems in production, including considerations for performance, observability and monitoring – a must.
Hands-on expertise with modern ML frameworks (PyTorch, Hugging Face Transformers) and GenAI toolkits (e.g., LangChain, LlamaIndex); proficiency with cloud ML platforms (AWS, GCP, Azure) and MLOps best practices
Experience with ASR – an advantage.
Team player.
