Data Science Analyst at Accenture

Job Description

• As a Data Science Analyst, you will design and implement advanced AI-driven solutions leveraging Generative AI (GenAI), Agentic AI, Machine Learning (ML), and Deep Learning to solve complex business challenges. This role combines technical expertise with strategic thinking to deliver impactful insights and scalable models across industries. 

Key Responsibilities 

• AI/ML Model Development: Build and deploy supervised and unsupervised ML models, including deep learning architectures (CNNs, RNNs, Transformers). Implement anomaly detection, time-series forecasting, and predictive analytics. 
• Generative & Agentic AI Solutions: Develop and optimize LLMs, RAG pipelines, and prompt engineering for enterprise use cases. Apply Agentic AI frameworks to create autonomous systems that adapt to dynamic environments. 
• Data Engineering & Processing: Perform data extraction, cleansing, and transformation using SQL, Python, and cloud-native tools. Collaborate with data engineers to ensure robust data pipelines. 
• Visualization & Communication: Create dashboards and visualizations using Power BI, Tableau, or similar tools. Translate technical insights into actionable recommendations for business stakeholders. 
• Innovation & Research: Stay ahead of emerging trends in AI/ML and integrate cutting-edge techniques into solutions. Experiment with multi-modal GenAI applications (text, image, audio, video). 

Qualification

Required Qualifications 

• Education: Bachelor’s or Master’s in Data Science, Computer Science, Statistics, Mathematics, or related field. 
• Technical Skills: Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Hands-on experience with Generative AI, LLMs, Agentic AI concepts, and Deep Learning. Familiarity with cloud platforms (Azure, AWS, GCP) and big data ecosystems (Databricks, Snowflake). 
• Soft Skills: Strong problem-solving and analytical mindset. Excellent communication and stakeholder engagement skills. 

Preferred Skills 

• Knowledge of MLOps, model governance, and bias mitigation. 
• Experience with containerization (Docker/Kubernetes) and ETL processes. 
• Exposure to industry-specific AI applications (finance, healthcare, retail).