Lead AI Feature Engineer
New
J
JobgetherAI and machine learning
Based in IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLMachine LearningData science
Requirements
- Have hands-on experience in data science, machine learning, feature engineering, and predictive modeling.
- Have experience developing production-quality features for machine learning solutions.
- Understand data preparation, feature extraction, dimensionality reduction, feature selection, statistical analysis, and model performance optimization.
- Be proficient in Python and SQL.
- Have experience with notebooks, machine learning libraries, and modern data-processing frameworks.
- Have experience building scalable data pipelines, feature-generation workflows, and reusable feature assets in cloud-based environments.
- Bring strong analytical and problem-solving abilities to identify valuable signals in complex data.
- Be able to communicate technical concepts clearly to technical and business stakeholders and collaborate with multidisciplinary teams.
- Feature stores, reusable feature repositories, or feature-engineering frameworks are a plus.
- Experience with gradient boosting, ensemble methods, Bayesian modeling, optimization algorithms, forecasting, or advanced statistical techniques is desirable.
- Experience in life sciences, diagnostics, healthcare, laboratory environments, advanced manufacturing, or other data-rich scientific industries is advantageous.
Responsibilities
- Discover, develop, and maintain features from enterprise, scientific, laboratory, and instrument data.
- Explore data and run feature-engineering and feature-selection experiments to identify signals and optimize model performance.
- Build reusable feature data products, feature stores, and feature-generation pipelines for multiple models and business environments.
- Establish practices for feature quality, lineage, monitoring, documentation, and lifecycle management.
- Keep feature assets reliable, traceable, scalable, and production-ready throughout their lifecycle.
- Partner with Data Scientists, AI Scientists, AI Engineers, Product Owners, and Architects to turn business and scientific challenges into reusable feature assets and predictive capabilities.
- Apply statistical and machine learning techniques to identify patterns and signals in complex datasets.
- Contribute to scalable data-processing and feature-engineering practices for analytics, machine learning, and AI solutions.
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