Manager - AI Data Scientist
Job Description
Inviting applications for the role of Manger, AI Data Scientist
We are seeking a highly skilled AI Data Scientist to design, develop, and operationalize advanced Machine Learning solutions across enterprise environments. This role focuses on classical and deep learning, experimentation, statistical modelling, and productiongrade ML pipelines - with handson responsibility for delivering robust, highperforming models using ML Studio.
Responsibilities
Lead Development of endtoend ML models including data preparation, feature engineering, training, validation, and optimization.
Conduct advanced statistical analysis and Forecasting techniques, classical ML modeling, and deep learning experimentation.
Build productionready ML workflows, pipelines, and automated training/evaluation jobs.
Utilize ML Studio for experiment tracking, ML Ops, model versioning, and deployment.
Collaborate closely with data engineering and platform teams for scalable model deployment.
Create interpretable models and deliver clear insights, visualizations, and business recommendations.
Monitor production models, troubleshoot performance issues, and continuously refine ML systems.
Ensure models follow enterprise standards for quality, fairness, and responsible AI.
Technical skills
Strong handson experience with ML Studio (Azure ML Studio or equivalent) for experimentation, ML pipelines, and deployment.
Solid understanding of Machine Learning, Deep Learning, and statistical modeling concepts.
Expertise in Python and experience with ML libraries (scikitlearn, PyTorch, TensorFlow).
Strong data processing and analysis skills using Pandas and NumPy.
Ability to build visualizations and insights using Matplotlib/Seaborn.
Experience deploying and managing models on cloud platforms (Azure/AWS/GCP).
Qualifications we seek in you!
Minimum Qualifications
Bachelor's degree in computer science, Statistics, Mathematics, or related field.
Good years of experience in applied Machine Learning.
Strong programming skills in Python and practical experience with ML platforms.
Mandatory handson experience with ML Studio for endtoend ML lifecycle.
Preferred Qualifications/ Skills
Experience with classical ML and deep learning across varied Finance / Sales / Marketing use cases.
Knowledge of model explainability, evaluation frameworks, and responsible AI practices.
Experience with ML Ops, CI/CD for ML, and model monitoring approaches.
Cloud certification in Azure/AWS/GCP (Data Scientist or ML Engineer track).