Analytics and Modeling Analyst
Designation: Analytics and Modeling Analyst
Qualifications:Any Graduation
Years of Experience:3 to 5 years
About Accenture
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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 Microsoft’s 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 Master’s degree in Computer Science, Data Science, Engineering, or related field. • 2–3 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.
Gurgaon
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