Advanced Data Scientist

Honeywell; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Honeywell Technologies seeks a data scientist/ML engineer in Bengaluru to translate business problems into mathematically framed objectives and scalable targets. You will craft production-ready code, design scalable data pipelines, and develop deep learning models across text, audio, and visuals for cloud deployment.

The role requires 4–6 years of experience in ML/DS, strong Python/OOP, and expertise in PyTorch/TensorFlow, Spark, and Hadoop ecosystems. Proficiency with AWS/Azure/GCP is expected.

Qualifications

• Bachelor’s, Master’s, or Ph.D. in a highly quantitative field (Math, Stats, CS, OR).
• 4–6 years of industry experience as a Data Scientist or ML Engineer with multimodal projects.

Responsibilities

• Translate business problems into mathematical objectives and optimisation targets.
• Develop production-grade code with clean design patterns for scalable models.
• Design scalable data pipelines for training and inference on large datasets.
• Build, train, and fine-tune deep neural networks across text, audio, and visuals.
• Architect and deploy ML applications on AWS/Azure/GCP.

Skills

Python

OOP

scikit-learn

PyTorch

TensorFlow

PySpark

Hadoop

AWS

Azure

GCP

NLP

Transformers

CNNs

Education

Bachelor’s/Master’s/PhD in a quantitative field

Tools

Python

scikit-learn

PyTorch

TensorFlow

Apache Spark (PySpark)

Hadoop (HDFS, Hive, MapReduce)

AWS

Azure

GCP

Job description

• 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.

Required Technical Skills & Competencies

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).
• Machine Learning: In-depth understanding of standard Machine Learning algorithms (Trees, Boosting, SVMs, GMMs) with the ability to explain the underlying loss functions and optimizations mathematically.
• Deep Foundations: Thorough understanding of Multi-Layer Perceptrons (MLPs), mathematical derivation of backpropagation, hyperparameter initialization strategies (Xavier, He), optimization variants (Adam, RMSProp), and advanced regularization techniques (L1/L2, Dropout, Batch Normalization).

3. Advanced Natural Language Processing (NLP)

• Sequential Networks: Hands-on experience with sequence modeling, including Word Embeddings (Word2Vec, FastText), RNNs, LSTMs, and GRUs.
• Transformer Ecosystem: Deep structural knowledge of the Transformer architecture (Self-Attention math, Multi-Head mechanisms).
• Pre-trained NLP Models: Experience implementing and fine-tuning encoder-only (BERT, RoBERTa) and decoder-only (GPT series) architectures.

4. Computer Vision (CV) & Document AI

• Spatial Networks: Deep understanding of Convolutional Neural Networks (CNNs), feature map mathematics, pooling operations, and advanced CV backbones.
• OCR & Document Processing: Proven track record building or customizing Optical Character Recognition (OCR) systems for complex text extraction pipelines.
• Vision Transformers: Familiarity with the adaptation of attention mechanics to visual tasks (ViTs, Swin Transformers).

Education & Experience

Qualifications

• Education: Bachelor’s, Master's, or Ph.D. in a highly quantitative field (Mathematics, Statistics, Econometrics, Computer Science, Physics, or Operations Research).
• Experience: 4 to 6 years of industry experience working as a Data Scientist or Machine Learning Engineer with a portfolio of complex multimodal projects.

About Us

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world’s most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector’s transition from automation to autonomy.

Job Info

• Job Identification 157101
• Job Category Data & Analytics
• Posting Date 09/08/2026, 03:55 PM
• Job Schedule Full time