Machine Learning Engineer – Classical ML and Predictive Analytics

Keysight Technologies; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A global technology firm in Singapore is seeking a Machine Learning Engineer to develop innovative, data-driven solutions for the manufacturing and semiconductor sectors. This role involves implementing anomaly detection systems, predictive maintenance models, and applying NLP techniques to analyze unstructured data. Candidates should possess a Bachelor's or Master's degree in a quantitative field and have 2-4 years of relevant experience. Strong programming skills in Python and familiarity with machine learning frameworks are essential. Join a collaborative team and contribute to high-impact projects.

Qualifications

• 2-4 years of hands-on experience in classical machine learning or data science roles.
• Strong programming skills in Python with version control (Git).
• Familiarity with anomaly detection and predictive modeling.

Responsibilities

• Develop anomaly detection systems for real-time monitoring of sensor data.
• Build predictive maintenance models using classical machine learning.
• Collaborate with data engineers and product teams in an Agile framework.

Skills

Classical machine learning techniques

Python programming

Scikit-learn

XGBoost

NLP techniques

Data manipulation with Pandas

Agile methodologies

Education

Bachelor's or Master's degree in Machine Learning, Computer Science, Data Science, Statistics, or a related field

Tools

Apache Spark

TensorFlow

PyTorch

Job description

Overview

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world‑class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award‑winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry‑first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

Responsibilities

We are looking for a talented and motivated Machine Learning Engineer with a strong foundation in classical machine learning techniques to join our innovative team in the manufacturing and semiconductor sectors. This role focuses on developing robust, data‑driven solutions to detect anomalies, predict maintenance needs, and monitor market dynamics, ensuring our clients maintain operational excellence and competitive edge.

As part of our analytics engineering group, you will tackle real‑world problems using numerical datasets from sensors and tests, while also applying NLP methods to extract insights from unstructured data sources. This position is ideal for an early‑career professional eager to contribute to high‑impact projects in a collaborative, fast‑paced environment, where precision and reliability are paramount.

• Design and implement anomaly detection systems to monitor sensor and test data in real‑time, identifying deviations that could indicate equipment issues or process inefficiencies in semiconductor manufacturing environments.
• Develop predictive maintenance models using classical machine learning algorithms, leveraging numerical datasets to forecast potential failures and provide actionable recommendations to customers, thereby minimizing downtime and optimizing resource allocation.
• Build and maintain monitoring pipelines for competitor pricing data scraped from online sources, incorporating drift detection mechanisms to ensure model reliability over time and alert on significant market shifts.
• Tackle a variety of classical ML challenges, including regression, classification, and clustering tasks, utilizing libraries such as XGBoost and Scikit‑learn to deliver scalable, interpretable solutions.
• Address NLP‑related problems by applying innovative approaches, including classical recurrent architectures like RNNs or LSTMs, to process and analyze textual data (e.g., from market reports or operational logs) alongside traditional ML workflows.
• Collaborate with data engineers, domain experts, and product teams in an Agile framework to iterate on models, conduct thorough validation, and deploy solutions into production.
• Perform rigorous model evaluation, hyperparameter tuning, and feature engineering to enhance accuracy and generalizability, with a focus on handling imbalanced datasets and ensuring ethical AI practices.
• Contribute to data pipeline improvements, potentially using tools like Apache Spark for large‑scale processing, and stay current with evolving ML methodologies to propose enhancements.

Qualifications

Must‑have qualifications

• Bachelor's or Master's degree in Machine Learning, Computer Science, Data Science, Statistics, or a related quantitative field.
• 2-4 years of hands‑on experience in classical machine learning (including deep learning) or data science roles, with demonstrated ability to build, train, and validate models from scratch.
• Proficiency in classical ML frameworks and libraries, including Scikit‑learn for core algorithms, XGBoost for gradient boosting, and experience with NLP techniques using RNNs/LSTMs (e.g., via TensorFlow or PyTorch).
• Strong programming skills in Python, with a focus on clean, maintainable code, version control (Git), and basic data manipulation using Pandas and NumPy.
• Familiarity with anomaly detection, predictive modeling, and concept drift monitoring in numerical datasets.
• Experience working in Agile/Scrum environments, including sprint‑based development and cross‑functional collaboration.
• Solid understanding of model evaluation metrics, cross‑validation, and QA processes for ML systems.

Strongly preferred

• Fluency in English.
• Exposure to industrial domains such as manufacturing, semiconductors, or sensor‑based analytics.
• Knowledge of big data tools like Apache Spark for distributed processing and handling large‑scale datasets.
• Prior work on NLP tasks integrated with classical ML, such as sentiment analysis or entity recognition in unstructured data.
• A portfolio of projects showcasing innovative solutions to real‑world ML problems.

Careers Privacy Statement Keysight is an Equal Opportunity Employer.