Key Accountabilities:
AI / Advanced Analytics
- Perform end-to-end AI-driven solutions from understanding business requirements, data discovery, and extraction to model development, evaluation, deployment, monitoring, and continuous improvement. Execute exploratory data analysis to support model development and answer business questions.
- Develop and deliver AI and data science solutions, including predictive models, prescriptive analytics, and intelligent automation, to drive business outcomes.
- Drive high-impact use cases that deliver measurable business outcomes (e.g., cost optimization, pricing insights, operational efficiency improvements)
- Collaborate with cross-functional teams (business, IT, data science) to develop, implement, and maintain AI solutions, including machine learning models and Generative AI use cases such as chatbots, knowledge assistants, and document processing solutions. Integrate external AI services where relevant (e.g., OpenAI APIs or cloud AI services).
- Partner with internal teams (business and IT) to deliver AI-enabled solutions (e.g., predictive insights, AI copilots) to enhance performance and audit reviews.
- Identify opportunities to leverage AI and advanced analytics to improve decision-making and business performance.
Business Intelligence
- Gather and analyse business requirements from various departments to develop effective BI solutions, including dashboards and reporting, ensuring alignment with business needs.
- Perform data analysis to extract actionable insights, identify trends, patterns, and anomalies, and support business decision-making through data storytelling
- Develop BI reports, dashboards, and data visualizations to deliver actionable insights to stakeholders using tools such as Microsoft Power BI or Tableau.
- Conduct ad-hoc analysis and deep-dive investigations to answer business questions and support strategic initiatives.
- Translate analytical findings into business insights and recommendations, including KPI definition, performance tracking, and opportunity identification
- Conduct training and presentations to end-users on reports, KPIs, dashboards, and AI-enhanced insights
Data Engineering
- Assist Data Engineering team in task related to design, build, and maintain the organization's data and AI infrastructure, including databases, data pipelines, feature stores, and reporting systems. Support scalable model deployment and lifecycle management.
- Identify data sources, perform data discovery, and provision data into the enterprise data warehouse to support analytics and AI use cases.
Process, Governance & Stakeholder Management
- Collaborate closely with stakeholders to identify opportunities for leveraging data and AI to drive business outcomes.
- Ensure timely implementation of planned initiatives across data, BI, and AI, including deployment and continuous improvement.
- Follow data governance, security, and AI governance policies, procedures, and standards to ensure data quality, model reliability, explainability, and compliance with regulatory requirements
- Ensure adherence to governance standards and maintain documentation for data, models, prompts, and reporting.
- Build consensus with subject matter experts and stakeholders on project scope and prioritization across hospitals and business units.
Skills and Knowledge
AI / Machine Learning, Generative AI & Agentic AI
- Experience building AI/ML and Generative AI (GenAI) applications, including model development, evaluation, deployment, and monitoring.
- Hands-on experience designing and developing Agentic AI solutions, including autonomous or semi-autonomous agents capable of reasoning, planning, and executing multi-step tasks using LLMs.
- Experience implementing agent-based workflows such as:
-Tool/function calling and API orchestration
-Multi-agent collaboration and task decomposition
-Memory management (short-term and long-term context)
-Human-in-the-loop controls and escalation
- Knowledge of LLM frameworks for building AI-powered and agentic applications.
- Experience integrating external AI services (e.g., OpenAI APIs) into enterprise-grade AI solutions.
- Understanding of model lifecycle management, evaluation techniques (including LLM/agent evaluation), and monitoring practices.
- Familiarity with vector databases (e.g., FAISS, Pinecone) and concepts such as embeddings and semantic search for Retrieval-Augmented Generation (RAG) use cases.
Data Analytics & BI
- Experience with cloud computing services on at least one major platform such as Microsoft Azure, Amazon Web Services, or Oracle Cloud Infrastructure (OCI), including data storage, compute, and security services.
Qualification and Experience
- Degree in Computer Science, Information Technology, or related field with 5–10 years of relevant experience.
- Strong hands-on experience with Databricks and Apache Spark.
- Experience developing data pipelines using ETL tools such as Azure Data Factory, Talend Studio, or Oracle Data Integrator.
- Experience working with structured and semi-structured datasets.
- Experience in healthcare or service industry is an advantage.
