Analytics and Modeling Analyst

Job Description

Skill required: Insight Engine - Analytics Insights Designation: Analytics and Modeling Analyst Qualifications: Any Graduation Years of Experience: 3 to 5 years About Accenture Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Technology and Operations services, and Accenture Song all powered by the worlds largest network of Advanced Technology and Intelligent Operations centers. Our 699,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. Visit us at www.accenture.com What would you do We are seeking a Machine Learning Data Scientist with 2 to 3 years of hands-on experience to join our growing Insights and Analytics team. The ideal candidate will be proficient in building ML models using supervised and unsupervised learning techniques, comfortable working with Python-based ML libraries, and experienced in data preparation workflows. Familiarity with Microsofts AI/ML ecosystem is a strong advantage. What are we looking for Preferred (Strong Advantage):Hands-on experience with Azure Machine Learning Studio and Automated ML.Familiarity with Azure Cognitive Services for vision, language, and decision tasks.Experience working with Microsoft Fabric and Synapse ML integration. Qualifications:Bachelor s or Masters degree in Computer Science, Data Science, Engineering, or related field.23 years of experience in applied machine learning and data science projects.Solid understanding of model training, validation, and deployment workflows.Experience working with version control (e.g., Git) and collaborative development environments.Nice to Have:Familiarity with tools that help automate and manage the process of building, testing, and deploying machine learning models (MLOps and CI/CD pipelines).Basic understanding of cloud-based architecture and APIs. Roles and Responsibilities: Develop and implement machine learning models using supervised and unsupervised techniques.Apply appropriate machine learning algorithms for prediction tasks (e.g., linear regression, decision trees, neural networks) and data segmentation (e.g., k-means clustering, hierarchical clustering) based on project needs.Perform data preprocessing, feature engineering, and model evaluation.Use Python libraries such as scikit-learn, TensorFlow, and others to build and test models.Analyse and manipulate structured datasets using notebooks (e.g., Jupyter).Collaborate with data engineers, analysts, and product teams to translate business problems into ML solutions.Document model performance, data flows, and process pipelines. Qualification Any Graduation