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In today's business environment, growth isn't just about building value-it's fundamental to long-term business survival. So how do organizations sustain themselves? The key is a new operating model-one that's anchored around the customer and propelled by intelligence to deliver exceptional experiences across the enterprise at speed and at scale. You will deliver breakthrough business outcomes for clients-by harnessing talent, data and intelligence to revolutionize their operating models.
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Operations is one of four services that make up one Accenture -the others are Strategy and Consulting, Interactive and Technology.
Job Description:
The Applied Artificial Intelligence (AI) Research Scientist- Knowledge Graphs role entails end to end design and development of a functional components of an AI solution within the Artificial Intelligence, Insights and Intelligence group. Active collaboration with clients/client teams and broader AI, business, product, engineering, testing, and deployment teams within Accenture is expected.
About the Team:
Artificial Intelligence team focuses on building scalable enterprise AI solutions that augment the humans/agents' capabilities. The focus is to make the business processes more efficient thereby enhancing the overall end user experience and delivery metrics. The team works with clients across diverse domains such as Finance and Accounting, Customer Engagement, Marketing, Talent and HR, Procurement, Content Moderation spanning across industries among many others.
Key Responsibilities:
- Translate business requirements into technical requirements
- Analyze data and prepare it to build AI models
- Design and prototype novel Knowledge Graph-based systems for applications in finance, social media, healthcare, HR, among others.
- Use graph inference algorithms and combine them with machine learning techniques in novel ways to solve problems for our clients.
- Benchmark the performance of AI models/ libraries that should be used for development
- Optimize the AI models built
- Integrate the functional component with the broader AI architecture
- Design target metrics of success for each AI deployment and test longitudinally
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Qualifications
Basic Qualifications:
- Ph.D. or Masters
- Minimum of 2 years of work OR academic experience Labeled Property Graph databases (e.g. Neo4J, TigerGraph, AWS Neptune, Azure Cosmos DB, or similar) and Graph query languages (e.g. Gremlin, SPARQL, Cypher, or similar)
- Minimum of 2 years of work OR academic experience in machine learning, artificial intelligence techniques including natural language processing (NLP), deep learning, or computer vision
- Minimum of 2 years of experience in programming languages (such as Python, Java or R) and database query languages (such as SQL)
- Preferred degree concentrations - Computer Science, Computer or Electrical Engineering, Statistics, Mathematics, Artificial Intelligence, Data Mining, Machine Learning or related scientific fields
- Prototyping and building knowledge graphs, graph taxonomies and ontologies
- Experience in Resource Description Framework (RDF)
- Ability to understand business context, generate ideas, and come up with creative solutions
- Solid familiarity with Graph Machine Learning methods particularly Graph Embeddings and Graph Neural Networks
- Experience combining graph-based inference algorithms with machine learning approaches
- Experience with temporal knowledge graphs
- Proven programming skills to enable rapid prototyping and implementation of solutions
- Experience in product development particularly applying Artificial Intelligence
- Team player mindset and ability to work in a global collaborative team environment
- Experience with communicating insights and presenting concepts to diverse audiences
- Proficient verbal and written communication skills in English
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