Data Scientist

As a Data Scientist, you will operate at the intersection of analytics, modelling, and enterprise-grade deployment. You'll translate business challenges into data-driven solutions, build and iterate on robust ML models, and operationalize them to deliver measurable impact across financial services workflows. The ideal candidate combines strong statistical foundations, hands-on engineering skills, business intuition, and the curiosity to work on emerging paradigms such as multi-agent systems, Model Context Protocol (MCP), retrieval systems, and AI orchestration.

Responsibilities:

• Build, improve, and optimize machine learning and deep learning systems.
• Own end-to-end model development and deployment, from data collection and pipeline creation to evaluation, validation, and production rollout.
• Design AI systems across multiple data types, including text, tabular, image, and document.
• Develop LLM-powered applications using prompt pipelines, RAG, agentic workflows, and domain-specific reasoning.
• Implement agentic AI patterns such as planning, tool use, orchestration, and autonomous decision support.
• Integrate AI systems with enterprise tools, APIs, and external platforms using MCP, function calling, and structured context frameworks.
• Build retrieval, reranking, and contextual memory pipelines to improve AI reliability and performance.
• Deploy AI systems in cloud and on-premises environments with a focus on scalability, observability, security, and governance.
• Analyze model outputs and system performance to drive continuous improvement and business impact.
• Collaborate with product, engineering, and leadership teams on execution, roadmap planning, and AI-native product development.
• Work across internal product initiatives and client deployments, from feasibility analysis to production implementation.
• Maintain high standards in clean coding, experimentation, documentation, and reproducible ML workflows.

Requirements:

• Strong foundation in machine learning, deep learning, statistics, and applied AI.
• 3+ years of hands-on experience building and deploying ML/DL systems in production.
• Strong experience with Python and frameworks such as PyTorch, TensorFlow, or equivalent.
• Comfortable working across text, tabular, categorical, image, and document-heavy datasets.
• Experience building, evaluating, and deploying end-to-end production ML systems.
• Familiarity with LLM applications, including prompt engineering, RAG, agentic workflows, and model evaluation.
• Good understanding of agentic AI concepts such as planning, memory, orchestration, tool use, and multi-agent systems.
• Exposure to MCP, tool-calling architectures, API integrations, or similar enterprise AI integration patterns.
• Experience with data pipelines, model serving, experimentation platforms, and cloud or on-prem deployment.
• Strong coding skills with clean, modular, and well-documented practices.
• Strong understanding of validation, error analysis, monitoring, and production improvement cycles.
• Ability to thrive in fast-evolving environments with shifting priorities.
• Strong ownership, problem-solving, and cross-functional collaboration skills.
• Strong communication skills with the ability to work directly with customers, product teams, and leadership.