About this role
Accenture seeks an AI Decision Science Consultant to deliver data science and AI solutions for automotive and industrial clients. The role applies traditional machine learning, generative AI, agentic AI and autonomous systems to improve processes and decision-making. Listed locations are Bengaluru (Bangalore), Gurugram (Gurgaon), Hyderabad and Chennai.
Responsibilities
- Develop, deploy and monitor production AI/ML models for predictive analytics, anomaly detection and process optimization.
- Apply generative models, including GPT, Stable Diffusion and DALL·E, to content generation, documentation, code synthesis and intelligent assistants.
- Build agentic automation, self-learning agents and decision-support systems, as well as autonomous solutions for predictive maintenance, supply chain optimization and robotic process automation.
- Work with structured and unstructured data, including IoT sensor readings, manufacturing logs and customer interactions. Optimize and fine-tune LLMs for business applications while supporting ethical, explainable AI.
- Use model-operations and AI orchestration tools for deployment, monitoring and retraining. Collaborate with engineers, business analysts and domain experts, and keep current with generative AI, autonomous AI and multi-agent research.
Requirements
The detailed description specifies 3–6 years of relevant experience in data science, machine learning or AI-related roles; the job header separately lists 5–10 years. Required skills include Python or R, generative AI, LLMs, RAG applications, and ML libraries or tools such as Scikit-learn, TensorFlow, Torch, Lang Chain or the OpenAI API. A bachelor's or master's degree in statistics, economics, mathematics, computer science or a related discipline with an excellent academic record, or an MBA from a top-tier university, is specified.
Preferred
Exposure to industrial or automotive firms or professional services is valued, but applicants from other domains are welcome. Additional desirable skills include Spark or Hadoop, AI explainability, bias detection, AI ethics, Edge AI and embedded-device deployment, reinforcement learning and AI-driven optimization.