Data Science Manager
Posted Updated
- Lead a team of exceptional Data Scientists and ML Engineers building next-generation AI solutions for financial markets. Drive innovation using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, and advanced Machine Learning to deliver real-world business impact
- As a Data Science Manager, you will lead a high-performing team of Data Scientists delivering AI-powered products that create measurable business value at scale
- This role combines technical leadership, people leadership, and strategic execution. 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
- You will partner closely with Product, Engineering, Research, and business partners to shape strategy, accelerate execution, and deliver impactful AI solutions
- Lead the design, development, evaluation, and deployment of production-grade AI and Machine Learning solutions
- Drive innovation in Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), Deep Learning, and Transformer-based architectures
- Define the technical vision for AI products, ensuring solutions are scalable, secure, maintainable, and aligned with business objectives
- Provide deep expertise in model development, experimentation, optimization, evaluation, and production deployment
- Establish standard methodologies for LLM evaluation, model benchmarking, AI quality measurement, and performance assessment
- Evaluate emerging AI technologies, foundation models, and third-party solutions to find opportunities for innovation and business value
- Guide architectural decisions across AI platforms, model-serving infrastructure, data pipelines, and MLOps/LLMOps capabilities
- Partner closely with Engineering teams to productionize AI solutions and drive operational excellence
- Build, mentor, and lead high-performing teams of Data Scientists and AI/ML practitioners
- Set clear goals, drive accountability, and support career growth and development
- Lead performance management, coaching, feedback, and talent development activities
- Foster a culture of innovation, collaboration, ownership, and continuous learning
- Drive hiring, onboarding, succession planning, and team growth initiatives
- Accelerate technical excellence through mentoring, technical reviews, and knowledge sharing
- Partner with Product, Engineering, and Business leaders to define AI strategy, roadmap, and priorities
- Drive execution through effective planning, prioritization, resource management, and delivery oversight
- Deliver high-quality AI solutions that create measurable business value
- Champion engineering excellence through guidelines, coding standards, experimentation, and governance
- Communicate technical strategy, risks, and recommendations clearly to technical and executive collaborators
- Promote responsible AI, model governance, compliance, and operational risk management
- Lead the development of next-generation AI products, demonstrating large-scale datasets, advanced AI models, and modern technologies to solve complex customer challenges
- Shape AI strategy, influence product direction, and build capabilities that deliver dynamic outcomes for customers worldwide
- Work at the forefront of Generative AI, LLMs, Agentic AI, Analytics, and Intelligent Search, helping define the future of AI innovation in financial markets
- Accelerate your leadership and technical career through continuous learning, innovation, and exposure to large-scale AI initiatives
- 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
Skills
- Agentic AI
- AI
- Analytics
- AWS
- Azure
- CI/CD
- Cloud
- Cloud Native
- Containerization
- Data Pipelines
- Data Science
- Deep Learning
- DevOps
- Distributed Computing
- Fine Tuning
- Generative AI
- Hugging Face
- Infrastructure as Code
- Kubernetes
- LangChain
- LLM
- LLMOps
- Machine Learning
- MLOps
- Neural Networks
- Observability
- OpenAI
- Performance Management
- Prompt Engineering
- Python
- PyTorch
- RAG
- Regulatory Compliance
- Reinforcement Learning
- scikit-learn
- Semantic Kernel
- Semantic Search
- Statistics
- Succession Planning
- TensorFlow
- Transformers
- Vector Databases