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 settings,
leveraging distributed computing and robust cloud infrastructure.

Required Technical Skills & Competencies
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).
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).
Machine Learning: In-depth understanding of standard Machine Learning
algorithms (Trees, Bo

📌 Advanced Data Scientist Bengaluru
🏢 Honeywell
📍 Bengaluru