Join Accenture's Global Network - Data & AI practice and empower clients to achieve high performance through data-driven insights. Our forward-thinking consultants leverage advanced analytics, from strategy to execution, to develop capabilities in data access, reporting, and predictive modeling. You will work within a worldwide network of driven colleagues experienced in leading statistical tools and applications, translating data into actionable strategies.

As part of our Data & AI practice, you'll gain continuous investment in your learning and growth. Collaborate with SaaS & PaaS experts, enhance your tech stack, and earn certifications. This role emphasizes sound analytical decision-making, understanding task relationships to overall projects, and executing initiatives for business performance improvement.

Develop and test AI algorithms tailored to complex business challenges. Expertly manage large databases, performing exploratory data analytics to uncover valuable insights. Investigate underlying information for problems, asking probing questions to identify root causes. Employ a systematic, inquisitive approach to unravel core issues. Collaborate effectively with internal teams and external consultants to achieve project goals. Build and manage data pipelines to streamline information flow for greater efficiency. Adapt proactively to evolving project requirements and embrace continuous learning to stay current with industry trends and advancements.

We seek individuals with a Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. Candidates should have experience in large Software or Platform organizations and a proven track record (2+ years) in AI projects, delivering successful outcomes. Essential hands-on exposure includes Generative AI frameworks (Azure Open AI, Vertex AI) and implementations. Strong knowledge of AI technologies such as Embedding, prompt engineering, NLP, and computer vision is required. Practical experience building and deploying statistical and machine learning models, including segmentation, predictive modeling, hypothesis testing, multivariate statistical analysis, time series techniques, and optimization, is crucial. The ability to work with extensive datasets and present findings to key stakeholders, coupled with data management skills using databases like SQL, is necessary. Proficiency in statistical packages like R, Python, Java, SQL, and Spark is expected. Familiarity with training large language models and fine-tuning them for specific applications is a plus. Experience with cloud platforms (AWS, Azure, Google Cloud) for deploying and scaling language models is advantageous. Excellent analytical and problem-solving skills with a data-driven mindset are fundamental. Proficiency in Excel, MS Word, and PowerPoint is also required.