Advanced Data Scientist

Honeywell Technologies; Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Honeywell Technologies is seeking a Data Scientist / ML Engineer to translate business problems into mathematically sound objectives and deployment-ready models. You will craft scalable data pipelines, implement multimodal deep learning systems, and deploy solutions on major cloud platforms.

Ideal candidates have 4–6 years in data science or ML engineering, strong Python, and hands-on PyTorch or TensorFlow experience, plus Spark/Hadoop for big data tasks.

Qualifications

• Bachelor’s or higher in a quantitative field; 4–6 years of data science or ML engineering experience with multimodal projects.
• Proficient in Python with strong OOP and clean coding practices.
• Experience with PyTorch or TensorFlow for building, training, and deploying models.
• Experience with Spark and Hadoop ecosystem for large-scale data processing and analysis.
• Cloud deployment experience on AWS/Azure/GCP for scalable ML applications.

Responsibilities

• Mathematical formulation: translate ambiguous business problems into mathematically sound framework objectives and optimisation targets.
• Production-grade engineering: write clean, modular code using production-level design patterns.
• Big data processing: design and manage scalable data pipelines for large datasets.
• Deep learning & vision development: build, train, and fine-tune neural networks across text, audio, and visuals.
• Cloud deployment: architect and deploy models on cloud environments.

Skills

Python

PyTorch

TensorFlow

Spark

Hadoop

Cloud platforms

Natural Language Processing

Computer Vision

ML algorithms

Mathematics

Education

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

Tools

scikit-learn

PySpark

HDFS

Hive

MapReduce

Job description

Key Responsibilities

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

• 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, 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).
• 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.
• 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.