The Role
Develops production-grade mathematical and machine learning solutions for large-scale, multimodal data. Responsibilities include designing data pipelines, building and fine-tuning deep learning models for text, audio, vision, NLP, OCR, and document AI, and deploying scalable models in cloud environments. Requires strong mathematical foundations, advanced Python and machine learning expertise, distributed computing experience, and 4–6 years of industry experience.
Summary Generated by Built In
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.
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.
Skills Required
- Bachelor's, Master's, or Ph.D. in Mathematics, Statistics, Econometrics, Computer Science, Physics, Operations Research, or another highly quantitative field.
- 4 to 6 years of industry experience as a Data Scientist or Machine Learning Engineer.
- Advanced Python proficiency, including object-oriented programming, clean coding standards, and software design patterns.
- Advanced scikit-learn experience for preprocessing, feature engineering, and baseline modeling.
- Expertise with PyTorch or TensorFlow for developing and training deep neural networks.
- Experience with Apache Spark or PySpark and the Hadoop ecosystem, including HDFS, Hive, and MapReduce.
- Experience deploying scalable machine learning applications on AWS, Azure, or GCP.
- Strong foundations in linear algebra, multivariable calculus, and probability theory.
- In-depth knowledge of machine learning algorithms, loss functions, and mathematical optimization.
- Thorough understanding of neural network architectures, backpropagation, initialization, optimization, and regularization.
- Hands-on experience with sequence modeling, embeddings, RNNs, LSTMs, GRUs, and Transformer architectures.
- Experience implementing or fine-tuning BERT, RoBERTa, GPT, or comparable pretrained NLP models.
- Deep understanding of CNNs, computer vision backbones, and vision transformers.
- Proven experience building or customizing OCR systems and complex document-processing pipelines.
- Portfolio of complex multimodal machine learning projects.
Honeywell Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Honeywell and has not been reviewed or approved by Honeywell.
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Retirement Support — Retirement benefits are anchored by a strong 401(k) match with clear vesting and annual funding mechanics. Plan administration and education resources further reinforce long‑term savings support.
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Leave & Time Off Breadth — Time away provisions include company holidays, flexible vacation for many exempt roles, and paid sick time. These policies contribute meaningful breadth beyond base pay.
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Parental & Family Support — Paid parental leave is available to all parents with flexible usage options, and certain family‑building supports are included. Birth mothers can coordinate leave with short‑term disability for extended coverage.
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The Company
What We Do
Honeywell is a Fortune 500 company that invents and manufactures technologies to address tough challenges linked to global macrotrends such as safety, security, and energy. With approximately 110,000 employees worldwide, including more than 19,000 engineers and scientists, we have an unrelenting focus on quality, delivery, value, and technology in everything we make and do.
