Job Description

Our client, a top-tier financial services institution, is looking for 2 hands-on Senior Data Scientist to design, build and deploy production-grade agentic AI solutions within their Financial Crime Operations function. You'll lead the design and delivery of an agentic digital assistant that unifies multiple data sources to deliver persona-driven, targeted management reporting and operational insights — all surfaced inside Microsoft Teams. This is a genuinely hands‑on build role: you'll be embedded in a cross‑functional squad (engineers, analytics, technical BAs) working alongside domain experts and solution architects, moving the business away from a sprawl of dashboards and into a single consumption layer offering automated nudges, coaching content, scenario modelling and high-risk alert support.

Key responsibilities n

Architecting and building agentic/LLM-driven features that pull, reconcile and synthesise data from multiple sources — think Tableau, SharePoint, Snowflake, Excel trackers and beyond.

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Developing persona-specific views for senior stakeholders (think Banking Group Execs, EGMs, GMs and team leaders), including insight-generation logic like performance nudges and coaching-content retrieval.

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Integrating retrieval/QA pipelines with existing knowledge systems and operational tooling.

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Working with backend engineers to define data pipelines, schemas and storage.

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Owning LLM model selection,



fine‑tuning, prompt engineering and evaluation for insight generation and conversational query features.

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Ensuring explainability, accuracy and safety for high-risk alert scenarios, including sensitive content handling.

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Partnering with product, UX and business stakeholders to translate persona needs into requirements and support rollout in Microsoft Teams.

About you n

6–10 years' experience as a Data Scientist, with direct exposure to Multi-Agentic AI or similar — this is a senior, individual‑contributor role with end-to-end delivery experience.

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Hands-on experience building agentic, multi-agent or LLM-driven systems, conversational AI, or orchestration layers.

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Solid ML fundamentals: supervised/unsupervised learning, recommender/insight systems, and applied prompt engineering/fine‑tuning for LLMs.

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Data engineering and integration experience across multiple sources (SQL, Snowflake, AWS data stack, enterprise-style platforms).

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Experience building search/retrieval systems (RAG, vector databases) and connecting to knowledge bases.

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Strong Python (pandas, scikit-learn, PyTorch/TensorFlow), API and cloud services experience (AWS preferred).

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Excellent stakeholder management skills, with the ability to work through ambiguous, unstructured problems in a cross‑functional setting.

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📌 Senior Data Scientist (Agentic AI) (Sydney)
🏢 JL Recruitment Pte.
📍 Sydney