Join Accenture's global data science group as a Senior Analyst in I&F Decision Science. This high-impact team drives innovation and transformation within Accenture Operations by leveraging machine learning, advanced analytics, and data science. You will tackle diverse and exciting business challenges, developing cutting-edge machine learning methods and data science solutions for next-generation offerings.

The team focuses on conceptualizing, designing, and developing innovative solutions across various Accenture Operations business towers, including Retail, Supply Chain, Customer Analytics, Finance & Accounting, Procurement, and Cloud Infrastructure. We employ a multi-faceted approach to project execution, adapting existing assets, exploring third-party and open-source solutions, and engaging in fundamental research to create novel approaches. Collaboration with Accenture Tech Labs and Accenture Open Innovation is key to our success.

In this role, you will be responsible for analyzing and solving increasingly complex problems. Your daily interactions will primarily be with peers within Accenture, with potential for engagement with clients and/or Accenture management. You will receive minimal direct instruction for daily tasks, with moderate guidance for new assignments. The decisions you make will impact your own work and potentially the work of others. This is an individual contributor role, with the possibility of overseeing a small work effort or team.

Key responsibilities include applying advanced statistical modeling and forecasting techniques to retail supply chain challenges. You will work with large datasets, develop machine learning models, and present findings using data visualization tools. A strong understanding of forecasting, time series analysis, and machine learning algorithms is essential.

We are seeking individuals with expertise in Retail Supply Chain, Databricks, Forecasting, Time Series, and Statistical Modeling. Prior experience with Oracle Retail Demand Forecasting (RDF) is highly valued. Proficiency in MS Excel and PowerPoint is required.

A solid understanding of at least four of the following techniques is essential: Classification, Regression, Clustering, Neural Networks, Dimensionality Reduction Techniques, Ensemble Modelling (random forest, boosted tree), Natural Language Processing, and Forecasting. Proven experience in Time Series Forecasting, Retail Supply Chain Demand Forecasting (in-season and pre-season), Location Planning, Assortment Planning, and Demand Planning for new and existing stores is crucial.

Candidates must possess strong communication and articulation skills in English. A basic understanding of various machine learning/data science models, data mining, and unstructured data analytics from corporate or academic research is expected. Professional experience of 1-4 years in Retail Forecasting projects is required, along with the ability to manage and navigate large datasets and think creatively to solve real-world business problems. Experience working in a global, collaborative team environment is also important. Statistical programming experience in Python and data visualization experience with Power BI are necessary.