oracle
Advanced Data Scientist
Honeywell
Bengaluru, IND
Full-time
Mid · 4+ years experience
Onsite
Discovered 1 weeks ago
Pythonscikit-learn (sklearn)PyTorchTensorFlowApache SparkPySpark
Free
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Pythonscikit-learn (sklearn)PyTorch
Key Skills for This Role
Pythonscikit-learn (sklearn)PyTorchTensorFlowApache SparkPySpark
Full Job Posting
Key Responsibilities
- Mathematical Formulation: Translate ambiguous business problems into mathematically sound framework objectives and optimisation targets.
- Production-Grade Engineering: Write clean, modular, and maintainable code using production-level design patterns to scale mathematical models.
- Big Data Processing: Design and manage scalable data pipelines to process massive datasets efficiently for model training and inference.
- Deep Learning & Vision Development: Build, train, and fine-tune complex neural networks across text, audio, and visual modalities.
- Cloud Deployment: Architect and deploy models to cloud environments, leveraging distributed computing and robust cloud infrastructure.
1. Tooling, Libraries & Software Engineering
Core Language: Advanced proficiency in Python with a strict adherence to Object-Oriented Programming (OOP) principles, clean coding standards, and design patterns.
Machine Learning Libraries: Advanced proficiency in scikit-learn (sklearn) for data preprocessing, feature engineering, and baseline modelling.
Deep Learning Frameworks: Core expertise in PyTorch (preferred) or TensorFlow for building, customizing, and training deep neural networks from scratch.
Big Data Ecosystem: Experience with Apache Spark (PySpark) and the Hadoop Ecosystem (HDFS, Hive, MapReduce) for handling, transforming, and querying large-scale distributed datasets.
Cloud Architecture: Experience building and deploying scalable machine learning applications on major cloud platforms ( AWS , Azure , or GCP ).
2. Core Mathematics & First-Principles ML
Foundational Math: Solid foundation in Linear Algebra (eigenvalues, SVD, matrix decompositions), Multivariable Calculus (partial derivatives, gradients, Jacobians), and Probability Theory (Bayesian inference, probability distributions, expectation maximization).
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