Senior Data Scientist (Agentic AI)

JL Recruitment Pte. Ltd.; Sydney; On-site; AUD 180,000 - 260,000; Full time

Job summary

Confidential Client in Sydney is seeking two hands-on Senior Data Scientists to design, build and deploy production-grade agentic AI solutions within Financial Crime Operations. You will lead the creation of an agentic digital assistant unifying data sources and delivering persona-driven management reporting inside Microsoft Teams.

This senior, individual-contributor role requires 6–10 years of experience with agentic AI or similar, hands-on systems, strong ML fundamentals and data integration

Qualifications

• 6–10 years' experience as a Data Scientist with multi-agent AI exposure.
• Hands-on experience building agentic, multi-agent or LLM-driven systems.
• Strong ML fundamentals across supervised/unsupervised learning and recommender systems.
• Data integration across SQL, Snowflake and AWS data stack.
• Experience with search/retrieval systems (RAG, vector DBs) and knowledge bases.

Responsibilities

• Architect and build agentic/LLM-driven features across data sources.
• Develop persona-specific views for senior stakeholders and insights.
• Integrate retrieval pipelines with existing knowledge systems.
• Collaborate with backend teams on data pipelines and schemas.
• Own model selection, prompt engineering and evaluation for insights.
• Ensure safety and explainability for high-risk scenarios.

Skills

Data Science

Agentic AI

LLM-driven

Python

APIs

Stakeholder mgmt

Cross-functional

Tools

Tableau

SharePoint

Snowflake

Excel

AWS

PyTorch

TensorFlow

SQL

scikit-learn

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

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

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.

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

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

Owning LLM model selection, fine‑tuning, prompt engineering and evaluation for insight generation and conversational query features.

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

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

About you

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.

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

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

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

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

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

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