Data Science Manager

Join our team as a Data Science Manager, where you will lead a high-performing team of Data Scientists and ML Engineers in building next-generation AI solutions for financial markets. You will drive innovation in AI and Machine Learning, establish engineering excellence, and develop exceptional talent while delivering production-grade solutions that solve complex customer problems. This role combines technical leadership, people leadership, and strategic execution, and you will partner closely with Product, Engineering, Research, and business partners to shape strategy, accelerate execution, and deliver impactful AI solutions.

- Lead a team of Data Scientists and ML Engineers to build AI solutions for financial markets. - Drive innovation in AI and Machine Learning, establish engineering excellence, and develop exceptional talent. - Partner closely with Product, Engineering, Research, and business partners to shape strategy and deliver impactful AI solutions.

- Are you an experienced Data Science leader with a passion for building and scaling AI/ML products? - The ideal candidate has a consistent track record of leading technical teams, scaling AI initiatives from concept to production, and fostering a culture of innovation, collaboration, and continuous improvement - Strong coaching, mentoring, performance management, and talent development capabilities - Proven track record of building, leading, and developing high-performing teams of Data Scientists, ML Engineers, and AI practitioners - Demonstrated success delivering large-scale AI initiatives from ideation to production with measurable business impact - Exceptional leadership, communication, and influencing skills with experience engaging senior leadership and executive collaborators - Experience leading multiple concurrent programs, balancing priorities, resources, and customer expectations - Advanced proficiency in Python and modern AI frameworks, including PyTorch, TensorFlow, Scikit-Learn, Hugging Face, LangChain, Semantic Kernel, and related ecosystems - Extensive experience designing, building, and deploying production-grade AI and Machine Learning solutions at scale - Hands-on experience with LLM evaluation, benchmark design, model validation, prompt engineering, guardrails, and AI quality assessment - Strong understanding of model observability, monitoring, evaluation frameworks, experimentation, reliability, and operational perfection - Good foundation in statistics, probability, optimization, experimentation, and applied machine learning - Expertise in cloud-native AI development using Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services, and modern cloud architectures - Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users - Deep expertise in LLMs, Generative AI, RAG, Agentic AI, Deep Learning, Neural Networks, and Transformer architectures - Experience implementing MLOps and LLMOps practices, including CI/CD, model lifecycle management, governance, and production operations at scale - Strong interested party leadership skills with a proven track record to drive alignment across Product, Engineering, Research, and Business teams - Ability to communicate complex technical concepts clearly to technical, business, and executive audiences - Known to work influencing technical strategy, product direction, and organizational decision-making - Experience leading teams building AI products for financial services, capital markets, research, analytics, or other data-intensive domains - Experience developing multi-agent systems, autonomous workflows, copilots, and intelligent AI assistants - Knowledge of reinforcement learning, fine-tuning, synthetic data generation, model compression, and advanced optimization techniques - Experience with Knowledge Graphs, vector databases, semantic search, retrieval systems, and Graph RAG architectures - Strong understanding of Responsible AI, model governance, AI safety, privacy, regulatory compliance, and risk management frameworks - Experience with DevOps, CI/CD, Kubernetes, Infrastructure as Code, containerization, and distributed computing platforms - Contributions to the AI community through publications, patents, conference presentations, research, or open-source projects - Master's or equivalent experience or PhD preferred - Bachelor's degree or equivalent experience in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, Artificial Intelligence, Machine Learning, or a related quantitative field