СингапурAI Specialist - Enterprise Decision Management
1 нед. назад
Сингапур
predictive analyticsbusiness rulesdecision automation
Lead design and delivery of intelligent decision automation solutions in financial services.
О компании
- , a Wipro company, is a global management and technology consultancy specialising in driving digital transformation across the financial services industry. Operating at the intersection of business and technology, combines innovative thinking with unrivalled industry knowledge to fast-track initiatives for banking and payments, capital markets, wealth and asset management, and insurance. Our ingenuity is brought to life through our Innovation Labs, award-winning Be Yourself at Work culture, and a diverse, talented team across more than 30 offices worldwide.
Будет плюсом
- Have spent a decade or more building, deploying, and evolving intelligent decision systems in financial services — with a track record that spans the full EDM lifecycle from predictive model development through to rules-driven operational execution and outcome measurement.
- Understand the complete toolbox of AI techniques available for decision problems — classical and modern — and have the experience and intellectual honesty to choose the right tool rather than default to whichever approach is currently receiving the most attention.
- Be as fluent discussing scorecard development, Gini coefficients, and population stability indices as they are discussing transformer architectures, prompt engineering, and RAG pipelines.
- Have deep domain expertise in insurance and/or wealth management decision logic, with secondary familiarity in banking or capital markets.
- Be comfortable operating as a senior individual contributor, a client-facing advisor, and a practice builder — often simultaneously.
- Thrive in 's flat, non-hierarchical culture and bring an entrepreneurial spirit to the challenge of building a high-value, differentiated capability in Asia.
- Experience with real-time and event-driven decision architectures, including complex event processing (CEP), streaming decision services, and sub-second decisioning requirements.
- Exposure to insurance platform ecosystems (Guidewire, Duck Creek, Majesco) or wealth platform ecosystems (Avaloq, FNZ, iCapital) and their integration with external decision and analytics services.
- Knowledge of process and decision automation platforms (Pega, Camunda, Appian) and how their native rules capabilities relate to — and differ from — dedicated BRMS platforms.
- Prior experience working across Singapore and Hong Kong markets, with an appreciation for the regulatory and competitive dynamics shaping AI adoption in both jurisdictions.
- Published thought leadership, conference presentations, or open-source contributions in the decision management, AI, or predictive analytics space.
Условия
- You will work at the cutting edge of intelligent decision automation in financial services, with clients who are genuinely committed to transforming how they make decisions at scale. At you will find:
- A work culture focused on innovation and building lasting value for clients and colleagues alike.
- Ongoing learning and development opportunities to deepen your expertise and grow as a recognised leader in enterprise decision management.
- A flat, non-hierarchical structure that enables you to work directly with senior partners and client executives from day one.
- A diverse, inclusive, and meritocratic culture where your depth of expertise and real-world impact define your reputation.
- The backing of a globally recognised brand and the resources of Wipro, combined with the agility and entrepreneurial spirit of a boutique consultancy.
Другое
- Every consequential moment in financial services — approving a policy, extending credit, flagging a transaction, recommending a portfolio — is a decision. The best financial institutions do not leave those decisions to chance, intuition, or inconsistent human judgement. They engineer them: combining predictive analytics, business rules, and optimisation into decision services that are fast, consistent, auditable, and continuously improving. That discipline is Enterprise Decision Management.
- As an AI Specialist in Enterprise Decision Management, you will lead the design and delivery of intelligent decision automation solutions for 's financial services clients across Asia. You will help clients move beyond point-in-time analytics and disconnected AI engines toward closed-loop decision systems: where predictive models inform rules, rules govern actions, outcomes are measured, and both models and rules are continuously refined as a result.
- This role demands genuine depth across the full AI and decision management spectrum — from classical techniques such as expert systems, case-based reasoning, and scorecards, through to modern machine learning, optimisation, and large language model integration. We are not looking for generalists who discovered AI in the last few years. We are looking for practitioners who have spent a career building, deploying, and improving intelligent decision systems in production financial services environments.
- Think of this as a forward-deployed role: you will be embedded with clients, building working solutions alongside their teams, and leaving behind capabilities that outlast the engagement. You will also play a defining role in building 's Enterprise Decision Management practice across Asia.
- Lead the design of end-to-end Enterprise Decision Management (EDM) architectures — connecting data ingestion, predictive model scoring, business rule execution, decision optimisation, and outcome measurement into coherent, production-grade decision services.
- Advise clients on decision strategy: identifying which decisions to automate, which to augment, and which to keep in human hands — and designing the governance frameworks to manage each appropriately.
- Model and decompose complex financial services decision domains — underwriting, claims adjudication, credit and affordability assessment, client suitability, fraud detection, and regulatory compliance — into structured decision architectures using DMN and related standards.
- Define the feedback loops and monitoring frameworks that allow decision systems to learn from outcomes — closing the loop between prediction, decision, action, and measurement.
- Design and oversee the development of predictive models — including scorecards, logistic regression, gradient boosting, and neural network approaches — that feed directly into operational decision flows.
- Apply prescriptive analytics and mathematical optimisation techniques (linear programming, constraint optimisation, genetic algorithms) to maximise decision outcomes at portfolio or population level, not just at the individual transaction level.
- Advise on champion/challenger frameworks, model validation approaches, and the governance of model changes within live decision systems — ensuring clients meet MAS, HKMA, and internal model risk management requirements.
- Apply classical AI techniques — expert systems, case-based reasoning, Bayesian inference — where they are the most appropriate and explainable tool for a given decision problem.
- Distinguish clearly between rule engine integration concerns (deployment architecture, service exposure, API design, DevOps pipelines for rules) and rule service design concerns (authoring, testing, governance, and versioning of business logic) — and bring demonstrated expertise in both.
- Design the interfaces between predictive model outputs and rules engine inputs — ensuring that model scores, segments, and recommendations are consumed correctly within decision flows and that the combined output is auditable.
- Integrate decision management capabilities with LLM and generative AI architectures — including patterns such as rules-based guardrails over LLM outputs, LLM-driven document understanding feeding structured rule evaluation, and conversational interfaces over decision services.
- Advise clients on where deterministic, rules-based approaches and probabilistic, model-based approaches are best combined — and design hybrid architectures that preserve the auditability and consistency that regulated financial services require.
- Drive business development activities including proposals, RFP responses, client workshops, and thought leadership on decision intelligence and AI in financial services.
- Mentor junior and mid-level team members and contribute actively to building 's decision intelligence practice, intellectual property, and accelerators across Asia
- Deliver robust, production-ready decision management solutions that demonstrably improve client decision quality, speed, consistency, and measurability.
- Ensure decision systems meet the governance, auditability, explainability, and model risk requirements of financial services regulators across Asia-Pacific, including MAS and HKMA guidelines on the use of AI in decision-making.
- Maintain and grow senior client relationships, acting as a trusted long-term advisor on decision intelligence strategy and roadmap.
- Uphold 's standards of quality, integrity, and professionalism across all client engagements.
- Contribute measurably to Asia's decision management practice through reusable assets, methodology development, and external thought leadership.
- Minimum 10 years of experience in decision management, AI engineering, or a closely related analytical discipline within financial services.
- Proven track record of delivering Enterprise Decision Management solutions in production environments — combining predictive analytics, optimisation, and business rules within operational decision flows.
- Strong domain knowledge in insurance and/or wealth management: underwriting decision logic, claims assessment, policy eligibility, suitability frameworks, KYC/AML rules, or client segmentation and next-best-action engines.
- Experience advising senior stakeholders — Chief Analytics Officers, Chief Risk Officers, Chief Data Officers — on decision automation strategy and the business case for intelligent decisioning investments.
- Hands-on experience with enterprise BRMS platforms in production: IBM Operational Decision Manager (ODM), FICO Blaze Advisor, Red Hat Decision Manager (RHDM), or IBM Drools — with the ability to distinguish and lead on both rule engine integration and rule service design.
- Proficiency in decision modelling standards: DMN (Decision Model and Notation), PMML (Predictive Model Markup Language), and their use in documenting, governing, and exchanging decision and model assets.
- Practical experience designing and implementing expert systems, including forward and backward chaining inference, Rete algorithm optimisation, and rule conflict resolution strategies.
- Working knowledge of case-based reasoning (CBR), Bayesian networks, constraint satisfaction, and genetic and evolutionary algorithms — with experience applying these techniques to real financial services problems.
- Experience designing and building traditional credit or insurance scorecards, including feature engineering, binning, weight-of-evidence encoding, logistic regression, and scorecard scaling.
- Proficiency in decision optimisation: linear and integer programming, constraint-based optimisation, and the application of optimisation to portfolio-level decision problems such as collections strategy, offer optimisation, or risk-based pricing.
- Working knowledge of machine learning model development, validation, and deployment lifecycle — including gradient boosting (XGBoost, LightGBM), ensemble methods, and neural networks.
- Experience designing champion/challenger frameworks, model monitoring pipelines, and population stability analysis for models operating within live decision systems.
- Working knowledge of LLM capabilities and limitations, with practical experience designing hybrid decision architectures that combine deterministic rule engines with generative AI components.
- Familiarity with AI explainability techniques (SHAP, LIME, counterfactual explanations) and their application in regulated financial services decision contexts.
- Experience with model risk management frameworks and the documentation requirements of MAS FEAT principles, HKMA guidance on AI, or equivalent regulatory standards.