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Accenture

Advises businesses and runs their technology

Data & AI - Multiple Teams

Data ScienceConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 5 years of experience in commerce, data-driven merchandising, or commercial analytics involving pricing, promotions, assortment optimization, personalization or recommendations, route-to-market, or related retail or consumer packaged goods capabilities.
  • Strong understanding of econometric and statistical modeling, including regression analysis, hypothesis testing, multivariate analysis, time series, and optimization, and ability to apply these techniques to commerce use cases such as pricing, promotions, assortment, demand, and targeting.
  • Expertise in Azure Machine Learning, SQL, R, Python, and PySpark; working experience with large language model application frameworks such as LangChain, LangGraph, retrieval-augmented generation, and vector retrieval.
  • Proficiency in nonlinear optimization and resource optimization.
  • Familiarity with design patterns for deploying and maintaining machine learning models in production.
  • Strong command of commerce, marketing, customer, product, pricing, promotion, and transaction data, and corresponding business processes in retail and consumer packaged goods.
  • Hands-on experience using Excel, Word, and PowerPoint for communication and documentation.
  • Hands-on experience in state space modeling and mixed-effects regression.
  • Working knowledge of natural language processing, large language models, LangChain, LangGraph, retrieval-augmented generation, vector retrieval, and multi-agent patterns.
  • Advanced task management knowledge and experience, including planning tasks, discussing and working on priorities, tracking work, and reporting progress.
  • Ability to think analytically and systematically when analyzing data, problems, and situations, and to notice discrepancies and inconsistencies.

Responsibilities

  • Lead the shaping and delivery of data, analytics, artificial intelligence, machine learning, and agentic artificial intelligence solutions for commerce clients, connecting commercial priorities with scalable solution architecture and delivery across pricing, promotion, assortment, personalization, recommendation, campaign activation, and next-best-action use cases.
  • Guide teams and senior client stakeholders through project phases, from data discovery and commerce problem definition through model and agent development, validation, integration, deployment, and handover.
  • Define data requirements for SONG Commerce analytics and Agentic Commerce capability.
  • Clean, aggregate, analyze, and interpret data, and carry out data quality analysis.
  • Apply market sizing, lift estimation, and experimentation and measurement approaches to pricing, promotions, campaigns, personalization, and other commerce decisions.
  • Apply nonlinear optimization techniques to pricing, promotion, assortment, offer, and resource-allocation use cases.
  • Use time-series, clustering, causal, and descriptive analytics to support merchandising and commerce intelligence, including demand, pricing, promotion, assortment, and customer and product decisioning.
  • Develop and validate artificial intelligence and machine learning models in Azure Machine Learning, and integrate model outputs into commerce decisioning and agentic workflows.
  • Develop and manage data pipelines.
  • Apply scalable machine learning architecture design patterns and tools for deploying and maintaining machine learning models in production, including cloud platforms for pipelining, deploying, and scaling elasticity models.
  • Guide architecture choices, enterprise integration, guardrails, and evaluation for Agentic Commerce solutions.
  • Manage senior client relationships and expectations; communicate commerce insights, agentic artificial intelligence solution choices, value implications, risks, and recommendations effectively.
  • Drive capability building and thought leadership in artificial intelligence-enabled and Agentic Commerce, including reusable patterns, accelerators, and propositions.
  • Manage client expectations and develop trusted relationships.
  • Maintain strong communication with key stakeholders.
  • Act as a strategic advisor to clients on data-driven and artificial-intelligence-enabled commerce decisions across pricing, promotion, assortment, personalization, recommendation, and customer activation.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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