Join Accenture's Global Network - Data & AI practice, where we empower clients to achieve high performance through data-driven insights. Our forward-thinking consultants leverage leading statistical tools and methods to transform data into actionable strategies, enhancing customer relationships and outperforming the competition.

This role is part of the HDC5A team, dedicated to driving Data & AI solutions for SaaS and PaaS clients. You will collaborate with global counterparts to identify growth opportunities and ensure successful client deliveries from offshore. Accenture is committed to continuous investment in your learning and growth, supporting you in expanding your tech stack and certifications while working with SaaS & PaaS experts.

Develop and test AI algorithms and techniques tailored to complex business problems. Proficiently manage large databases and conduct exploratory data analysis to extract valuable insights. Investigate prior information related to issues, asking probing questions to identify root causes. Employ a systematic problem-solving approach, exploring known avenues and asking insightful questions. Collaborate with internal teams and external consultants to achieve project objectives. Develop and manage data pipelines for enhanced information flow efficiency. Adapt flexibly to changing project requirements and continuously seek learning opportunities to stay updated with industry trends.

We are seeking individuals with a Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. Prior experience working with large Software or Platform organizations is essential. You should have proven experience (2+ years) in AI projects, demonstrating successful outcomes. Hands-on exposure to Generative AI frameworks (Azure Open AI, Vertex AI) and implementations is required, along with strong knowledge of AI technologies such as Embedding, prompt engineering, NLP, and computer vision.

Proficiency in building and deploying Statistical Models/Machine Learning, including segmentation, predictive modeling, hypothesis testing, multivariate statistical analysis, time series techniques, and optimization is crucial. The ability to work with large datasets, present findings to stakeholders, and manage data using databases like SQL is necessary. Familiarity with statistical packages such as R, Python, Java, SQL, and Spark is expected. Experience with cloud platforms like AWS, Azure, or Google Cloud for deploying and scaling language models is also required. Excellent analytical and problem-solving skills with a data-driven mindset, and proficiency in Excel, MS Word, and PowerPoint are essential.