Join Accenture's Global Network - Data & AI practice and help clients achieve high performance through data-driven insights. This role focuses on developing AI solutions for SaaS and PaaS clients, working with a global network of experts.

As part of the Data & AI team, you will contribute to driving innovative solutions, collaborating with offshore and onsite counterparts, and delivering impactful results. Accenture is committed to investing in your continuous learning and growth, supporting you in expanding your tech stack and obtaining certifications. This position offers an excellent opportunity to understand the significance of sound analytical decision-making within a business performance improvement context.

Develop and test AI algorithms tailored for complex business challenges. Proficiently manage large databases, performing exploratory data analysis to extract valuable insights.

Investigate root causes of problems by thoroughly reviewing past information and asking probing questions. Employ a systematic problem-solving approach, exploring various avenues and seeking clarification.

Collaborate effectively with internal teams and external consultants to achieve project objectives. Develop and manage data pipelines to enhance information flow and efficiency. Demonstrate flexibility in adapting to evolving project requirements and actively pursue continuous learning to stay abreast of industry trends.

We seek individuals with a Bachelor's or Master's degree in computer science, engineering, data science, or a related field. Experience working with large Software or Platform organizations is essential.

Candidates should possess a minimum of 2 years of proven experience in AI projects, demonstrating successful outcomes. Hands-on exposure to Generative AI frameworks like Azure OpenAI and Vertex AI is required, alongside strong knowledge of AI technologies including Embedding, prompt engineering, NLP, and computer vision.

Proficiency in 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 stakeholders, along with data management skills using SQL, is also necessary.

Expertise in statistical packages such as R, Python, Java, SQL, and Spark is expected. Familiarity with training large language models and fine-tuning them for specific applications, as well as experience with cloud platforms like AWS, Azure, or Google Cloud for model deployment, is highly valued. Excellent analytical and problem-solving skills, coupled with a data-driven mindset, are fundamental. Proficiency in Excel, MS Word, and PowerPoint is also required.