About the role
Introduction We are seeking a client-facing Data Science Consultant with AI/ML expertise to design, build, and deploy data science solutions for financial services customers. You will translate complex business challenges into AI use cases, develop and operationalize models (including LLM/NLP, graph, supervised and unsupervised learning), and integrate them with SymphonyAI products and customer ecosystems. You will lead discovery, deliver PoCs and pilots, establish robust MLOps and model governance, and ensure successful adoption and outcomes. Job Description Key
Responsibilities Client engagement and solution design Lead discovery workshops to elicit
requirements, define success metrics, and translate business problems into AI solutions and delivery plans. Provide innovation, leadership and influence in the design of client-centric solutions that are aligned with the SymphonyAI’s product offering. Create SOW inputs, estimates, and technical approach for services engagements; support pre-sales with demos, POVs, and solution narratives. Manage the client review and sign-off process for the suite of functional documents and artefacts. Take overall responsibility for the documentation of functional artefacts. Model development and delivery Build and evaluate models for key financial services use cases (e.g., anti-money laundering, fraud detection, alert triage, trading surveillance, credit risk, customer intelligence). Apply advanced techniques: NLP/LLMs (RAG, summarisation, extraction, classification), graph/network analytics, time series, anomaly detection, and ensemble methods. Implement explainability (e.g., SHAP, LIME), bias/fairness testing, performance monitoring, and model risk controls to meet regulatory and enterprise standards. Deliver high-quality PoCs and pilots; productionise with engineering using robust MLOps practices (CI/CD, model registry, experiment tracking, feature management). Data engineering and integration Work with client and internal teams to profile, transform, and validate data; build performant pipelines using SQL/Spark and orchestration tools. Integrate AI services with SymphonyAI products and client systems via APIs, microservices, and event streams; optimize for scalability, latency, and cost. Implement retrieval components (e.g., vector databases) and prompt orchestration frameworks for LLM-based solutions. Change management and enablement Plan and execute user acceptance testing, training, and knowledge transfer to drive adoption and embed AI in business workflows. Mentor customer and internal teams; contribute reusable assets (templates, prompts, pipelines, notebooks) to accelerate future engagements. Provide thought leadership by sharing best practices and feedback to product and R&D for roadmap influence. Key
