Accenture Hiring 2026 – Decision Science Associate | AI, Data Science and Generative AI

Accenture is inviting applications for the position of Decision Science Practitioner Associate, listed under AI and Data Science. The job listing identifies the position as a full-time opportunity with 0–2 years of experience. The job number is R00361602, and Bengaluru is the preferred location, with Kolkata and Gurugram also listed as possible locations.

This opportunity may be suitable for candidates interested in Generative AI, Python programming, SQL, prompt engineering, REST APIs, AI application development, and cloud-based AI services. The role involves supporting experienced practitioners in developing AI-powered solutions, preparing knowledge sources, testing AI application outputs, assisting with retrieval-augmented generation (RAG), and contributing to technical documentation and deployment activities.

Candidates with a relevant bachelor's or master's degree and practical exposure to AI, machine learning, data science, or related engineering work may find this role worth exploring. The position emphasises hands-on contributions, so applicants should be prepared to explain the business problem they worked on, their individual contribution, and how they tested their solution.

Job Overview

Company Name: Accenture

Job Title: Decision Science Practitioner Associate

Job Category: AI and Data Science

Management Level: Associate – Level 12

Job Number: R00361602

Job Location: Bengaluru preferred, Kolkata, or Gurugram

Employment Type: Full-time

Experience: 0–2 years

Educational Qualification: Bachelor's or master's degree in a relevant quantitative or engineering discipline

Mandatory Skills: Python, SQL, Generative AI fundamentals, prompt engineering, APIs

Preferred Skills: RAG fundamentals, agentic AI concepts, LangChain, LangGraph, cloud AI platforms, and machine learning fundamentals

Cloud Platforms: Azure and/or AWS exposure

Salary: Not specified in the supplied job description

Application Deadline: Not specified in the supplied job description

About Accenture

Accenture is a global professional services company that works across technology, consulting, operations, and business transformation. It helps organisations use technology and data to improve business processes, develop digital capabilities, and address complex business challenges.

Artificial intelligence and data science have become important areas of enterprise technology. Organisations are exploring how AI can support information retrieval, automate repetitive tasks, improve analytical workflows, and help employees access relevant business knowledge.

Generative AI is one part of this transformation. It enables applications to produce responses and other outputs based on user instructions and the information available to the system. Businesses can combine large language models with APIs, enterprise data, retrieval systems, and cloud services to build applications for specific business needs.

The Decision Science Practitioner Associate role is designed to support this type of work. According to the supplied description, associates will work with experienced practitioners and contribute to AI application development, knowledge retrieval, analytics, testing, and solution delivery while developing their expertise in emerging AI technologies.

Understand the Job Role

The Decision Science Practitioner Associate position combines programming, Generative AI, data handling, application testing, and cloud technology. It is not described as a role focused exclusively on theoretical machine learning or traditional data analysis. The supplied responsibilities emphasise helping build AI-powered applications and ensuring that those applications are tested, documented, and prepared for delivery.

Candidates should understand the relationship between the technical components mentioned in the job description. Python can be used to build application logic and integrations. APIs allow applications to communicate with other services. Prompt engineering helps guide the behaviour of language models. RAG connects language models with relevant external knowledge, while cloud AI services provide infrastructure and capabilities for developing and running AI applications.

The job description also emphasises working under the supervision of experienced team members. Associates are expected to contribute to development and testing activities while learning established engineering practices, enterprise security standards, and deployment processes.

Generative AI and Large Language Model Applications

One of the main responsibilities is supporting the development of applications powered by large language models (LLMs). These applications may use prompts, APIs, and established frameworks to perform tasks involving language, information retrieval, or conversational interaction.

A large language model can generate responses based on an input prompt and the context available to it. Building a useful application requires more than simply sending a question to a model. Developers may need to structure inputs, integrate the model with other services, handle errors, check responses, and evaluate whether the output meets the intended requirements.

The supplied description asks associates to support this work rather than independently lead every stage of development. Candidates should therefore be comfortable with the fundamentals and ready to learn from senior practitioners.

Prompt Engineering

Prompt engineering involves designing and refining instructions provided to an AI model so that it produces useful and relevant outputs. A prompt may specify the task, provide context, define the expected response format, or establish constraints for the model.

Candidates should understand how prompt structure can affect the response. They should also recognise that a well-written prompt does not guarantee a correct answer. AI outputs need to be evaluated against the application's requirements, especially when the application supports business decisions or uses enterprise information.

Practical preparation can include building a small application that accepts a user question, sends a structured prompt to a model through an API, and evaluates whether the response follows the required format.

Retrieval-Augmented Generation (RAG)

The job description identifies RAG fundamentals as a preferred skill and specifically mentions preparing, organising, and validating knowledge sources for RAG solutions.

RAG combines a retrieval process with a language model. Instead of relying only on information already learned during model training, a RAG application retrieves relevant information from an available knowledge source and uses that information as context when generating a response.

A typical RAG workflow may involve collecting documents, cleaning and organising the content, dividing it into useful sections, generating embeddings, retrieving relevant passages, and supplying those passages to the language model. The exact architecture depends on the application.

Associates may help prepare knowledge sources, check whether the retrieved information is relevant, validate the quality of the data, and test whether the generated answer accurately reflects the available context.

Candidates preparing for this role should understand the purpose of retrieval, embeddings, vector databases, document preparation, and answer validation. These concepts are useful preparation areas, although the supplied job description does not require a particular vector database or RAG framework.

Conversational AI and Agentic AI

The responsibilities include assisting with conversational AI applications and agent workflows under the guidance of senior team members.

Conversational AI applications allow users to interact with a system through natural-language exchanges. Depending on the design, the application may answer questions, retrieve information, or call an external service to complete a task.

Agentic AI concepts involve systems that can use tools, follow a sequence of steps, and coordinate actions toward a goal. Tool calling and workflow orchestration can help an AI application interact with APIs or other components rather than merely generating text.

Candidates should understand the difference between a simple question-and-answer application and a workflow that can retrieve information or invoke tools. The job description lists agentic AI as a preferred area, so applicants should distinguish between concepts they understand and frameworks they have actually used.

Testing and Validation of AI Outputs

Testing is a significant part of the position. The description states that associates will perform functional testing and validate AI application outputs using defined test cases.

AI applications can produce incorrect, incomplete, inconsistent, or irrelevant responses. Testing helps identify these problems and determine whether the application behaves as expected.

A test case might check whether an application answers a question using the correct knowledge source, follows a required output format, handles missing information appropriately, or responds safely when a request falls outside the intended scope.

Candidates should understand how to define expected results, execute tests, record observed behaviour, identify defects, and document findings. They should also recognise that evaluating AI applications may require checking the relevance and accuracy of a response rather than simply determining whether the software ran without an error.

Cloud Platform Expertise

The job description mentions working with Azure and/or AWS AI services under supervision. It also includes supporting environment configuration, application testing, monitoring, security, and access management.

Cloud platforms provide services and infrastructure for developing, deploying, and operating applications. Depending on the project, AI development may involve hosted model services, application environments, identity controls, monitoring tools, or other cloud resources.

The vacancy does not specify a single mandatory cloud certification or exact service name. Candidates should focus on understanding basic cloud concepts and any AI services they have genuinely used. Familiarity with one platform may help provide a foundation for learning another, but applicants should not claim hands-on experience they do not possess.

Enterprise security is also important. AI applications may interact with business systems or confidential information, so developers need to understand access controls and follow approved security practices. The role specifically mentions learning and applying enterprise security and access management standards.

Python Development and API Integration

Python is one of the mandatory skills listed for the role. Associates are expected to develop Python-based components and integrate them with APIs and enterprise systems.

Candidates should revise Python fundamentals, including variables, data types, functions, collections, loops, conditionals, exception handling, and working with modules. They should also understand how to organise code into reusable components and troubleshoot common errors.

The description also names JSON and REST APIs. JSON is a common format for exchanging structured information between applications. REST APIs allow systems to communicate through defined endpoints and HTTP requests.

A useful practice project could involve a Python application that accepts input, sends a request to an API, processes the returned JSON data, handles errors, and presents the result clearly. If the project uses an AI service, candidates should also explain how they tested the output and handled unexpected responses.

SQL and Data Handling

SQL is another mandatory skill. It is commonly used to retrieve and analyse structured data stored in relational databases.

Candidates should revise SELECT statements, filtering with WHERE, sorting, aggregate functions, GROUP BY, joins, and basic subqueries. Understanding tables, relationships, keys, and data types can also help when working with enterprise information.

In an AI application, data quality can affect the usefulness of the result. Applicants should understand the importance of checking records, handling missing or inconsistent values, and ensuring that the information supplied to an application is appropriate for the task.

The supplied description does not specify a particular database technology, so candidates should concentrate on transferable SQL fundamentals unless additional recruitment instructions identify a specific system.

Git and Software Engineering Practices

The role includes using Git and standard development practices for source code management and collaboration. Git helps developers track changes, maintain versions, and collaborate on a shared codebase.

Candidates should understand basic operations such as creating a repository, staging and committing changes, reviewing history, and working with branches. They should also be familiar with writing readable code, maintaining useful documentation, and testing changes before they are delivered.

Docker, CI/CD, and software development lifecycle practices are listed as preferred areas. Applicants who have used these tools in academic or personal projects can describe their actual contribution and explain how the tools supported development or deployment.

Documentation, Troubleshooting, and Deployment Support

The position also includes creating technical documentation and supporting testing, troubleshooting, and deployment activities.

Documentation helps other team members understand an application's purpose, configuration, dependencies, expected behaviour, and known limitations. Troubleshooting requires examining errors, reproducing issues, reviewing logs or responses, and identifying possible causes.

Deployment activities may involve preparing an application for an environment where it can be tested or used. The exact deployment responsibilities and tools for this vacancy are not specified, so candidates should avoid assuming that the associate will independently manage production deployments.

Match Your Skills

The mandatory technical skills listed are Python, SQL, Generative AI fundamentals, prompt engineering, and APIs. Applicants should assess their practical ability in each area rather than relying only on familiarity with terminology.

Python and Programming Fundamentals

Candidates should be able to explain their code, describe why they selected a particular approach, and troubleshoot basic problems. A small, working Python project is often a useful way to demonstrate practical knowledge.

SQL and Data Concepts

Applicants should practise retrieving and validating structured information. They should be able to explain joins, filtering, grouping, and how they would check data quality.

Generative AI and Prompt Engineering

Candidates should understand what an LLM does, how prompts influence output, and why responses need to be validated. They should be able to describe an AI application they have built or studied, if applicable.

APIs and JSON

Applicants should understand how an application sends requests to an API and processes a response. Knowledge of HTTP methods, status codes, request parameters, authentication concepts, and JSON structures can be useful preparation.

RAG and AI Frameworks

RAG fundamentals, LangChain, and LangGraph are listed as preferred skills or exposure areas. Candidates who have used these technologies should explain what they built, why the framework was useful, and how they evaluated the result. Candidates without this experience can focus on understanding the underlying concepts.

Cloud and Machine Learning Fundamentals

Exposure to Azure or AWS AI services is relevant to the position. Machine learning libraries such as scikit-learn, TensorFlow, and PyTorch are also listed as preferred. Candidates should be accurate about which libraries and services they have actually used.

Communication and Analytical Thinking

The description highlights communication, analytical thinking, and collaborative problem-solving. Candidates should practise explaining technical decisions in plain language and describing how they approached a problem, tested a solution, and improved it based on the results.

Check Your Eligibility

The supplied job description lists an experience range of 0–2 years and specifies hands-on work in AI/ML, data science, Generative AI, or related engineering.

The stated educational requirement is a bachelor's or master's degree in a relevant quantitative or engineering discipline. The description does not provide a complete list of accepted degree specialisations, minimum academic percentages, eligible graduation batches, or backlog criteria. Candidates should not assume these additional conditions without confirmation.

The listed locations are Bengaluru, which is preferred, along with Kolkata and Gurugram. Applicants should consider whether they can work in one of these locations and confirm the final location arrangements through the recruitment process.

The job description emphasises hands-on contributions to AI solutions. Candidates should be ready to explain the business problem their project addressed, their personal contribution, and how they tested the solution. Merely listing AI tools on a resume may not be sufficient to demonstrate the practical experience described in the posting.

Be Ready With Your Documents

Candidates should prepare an updated resume that clearly presents their educational background, technical skills, projects, internships, and relevant experience.

For each AI or data science project, explain the problem, the approach taken, the technologies used, the candidate's individual contribution, and the testing process. If the project involved an LLM, RAG, Python, SQL, or an API, describe how those components were used and what limitations were encountered.

Applicants should keep their academic details and relevant supporting documents available. The supplied job description does not provide a specific document checklist, so candidates should follow the requirements communicated during recruitment.

Candidates should ensure that every skill listed on the resume can be explained honestly. If they have studied a framework but have not built a project with it, they should distinguish conceptual familiarity from hands-on experience.

Project Preparation for the Interview

The description explicitly asks candidates to demonstrate hands-on contributions and explain the business problem, their contribution, and how the solution was tested. Preparing a project explanation is therefore especially important.

A useful structure is to begin with the problem and why it mattered. Explain who would use the solution and what outcome it was intended to support. Next, describe the application architecture, the data or knowledge sources, the technologies used, and the candidate's own responsibilities.

Then explain how the solution was tested. This may include checking expected outputs, validating retrieved information, evaluating API responses, testing error conditions, or comparing results against known examples. Candidates should discuss any limitations and what they would improve if they continued the project.

For a Generative AI project, be prepared to explain how prompts were designed, how the application used model outputs, and how incorrect or unsupported answers were handled. For a RAG project, explain how documents were prepared and retrieved and how the relevance of the retrieved information was checked.

For a Python and SQL project, explain the data flow, the queries or transformations used, and how data correctness was verified. Clear explanations of real work are more valuable than claiming familiarity with a long list of tools without practical understanding.

How to Prepare for the Recruitment Process

The supplied description does not specify the selection rounds, assessment format, or interview stages. Candidates should not assume a particular test or number of interviews.

A practical preparation plan is to revise Python and SQL fundamentals first, because both are mandatory skills. Review prompt engineering, LLM application concepts, JSON, REST APIs, and the basic architecture of an AI-powered application.

Next, study RAG, embeddings, vector databases, agentic AI, and tool calling at a conceptual level. Candidates who already have hands-on experience with LangChain, LangGraph, Azure, AWS, or machine learning libraries should revise the projects in which they used those tools.

Finally, prepare to discuss a real project in detail. Practise describing the problem, the technical approach, individual contribution, test cases, results, limitations, and lessons learned. Be prepared to explain technical concepts clearly and answer follow-up questions about the choices made during development.

Frequently Asked Questions

What is the job title?

The detailed job description identifies the role as Decision Science Practitioner Associate, under the AI and Data Science category.

What is the job number?

The job number is R00361602.

Which company is hiring?

Accenture.

What is the experience requirement?

The listing states 0–2 years of experience and describes hands-on work in AI/ML, data science, Generative AI, or related engineering.

Which locations are available?

Bengaluru is preferred. Kolkata and Gurugram are also listed.

What are the mandatory technical skills?

Python, SQL, Generative AI fundamentals, prompt engineering, and APIs.

Which skills are preferred?

Preferred areas include RAG fundamentals, agentic AI concepts, LangChain or LangGraph exposure, cloud AI platforms, and machine learning fundamentals. The description also mentions vector databases, embeddings, Git, Docker, CI/CD, and machine learning libraries.

What educational qualification is required?

A bachelor's or master's degree in a relevant quantitative or engineering discipline is listed. The complete list of accepted degrees is not provided in the supplied description.

Is this role suitable for freshers?

The listing states 0–2 years of experience, but it also mentions hands-on work in relevant technical areas. Candidates with limited experience should carefully assess whether they can demonstrate the practical contributions described in the posting.

Is prior LangChain or LangGraph experience mandatory?

These frameworks are listed under preferred skills. The mandatory skills section instead specifies Python, SQL, Generative AI fundamentals, prompt engineering, and APIs.

Does the role involve cloud technology?

Yes. The description mentions working with Azure and/or AWS AI services under supervision. Exposure to cloud AI platforms is included in the technical requirements.

What is the salary package?

The supplied job description does not disclose the salary.

What is the application deadline?

The supplied description does not specify an application deadline.

What should candidates prepare for the interview?

Candidates should be ready to explain a hands-on AI solution, including the business problem, their personal contribution, the technologies used, and how the solution was tested.

Final Thoughts

Accenture's Decision Science Practitioner Associate opening is relevant to candidates who want to develop their skills in Generative AI, data science, Python, SQL, APIs, and cloud-based AI applications. The role combines development support, knowledge retrieval, testing, documentation, and collaboration with experienced practitioners.

The strongest preparation focus is on the mandatory skills and the ability to demonstrate practical work. Candidates should be ready to explain how their projects solve a problem, how they built or integrated the solution, and how they evaluated its results. Familiarity with RAG, agentic AI, LangChain, LangGraph, cloud AI services, and machine learning libraries can provide additional context where that experience is genuine.

Important Notice

This article is based on the job description provided. Salary, application deadline, complete degree eligibility, and selection stages were not specified and have not been assumed. Job details may change, so candidates should confirm the current vacancy and recruitment requirements through the relevant hiring communication. Job Seekers Hub shares job information for awareness and does not guarantee selection.

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