Senior Data Scientist – ML Product Development

Particle Measuring Systems • Niwot, CO

2d

About The Position

Do you want to be part of a business that genuinely values entrepreneurialism, innovation and individual accountability? We focus on our customers and are proud of the difference our technology makes. We partner with some of the biggest manufacturing companies in the world and our technical innovations are used to enhance well-known brands across multiple industries. About Us Established in 1972, Particle Measuring Systems is a global leader for micro-contamination monitoring equipment improving the performance of clean manufactures in the semiconductor and pharmaceutical industries. We’re a growing technology company in Niwot, Colorado, the heart of the Rocky Mountains. We offer an exceptional and rewarding work environment in a great place to live. Our employees enjoy challenging projects in the development and manufacture of light scattering particle counters and diverse technologies and applications. Your Impact We’re looking for a Data Scientist who is passionate about building machine learning (ML) solutions that directly power and enhance our products. You’ll collaborate closely with product managers, engineers, and designers to design, train, and deploy ML models that create meaningful user experiences, drive personalization, and improve decision-making at scale. The Role This role sits at the intersection of data science, software engineering, and product innovation — perfect for someone who loves seeing their models come to life in real-world applications. You’ll have the opportunity to see your ML work directly shape user experiences and business outcomes in a collaborative, data-driven culture that values innovation and continuous learning.

Requirements

• Master’s or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, or related field (or equivalent experience).
• 3+ years of experience building and deploying ML models in production environments, with 5-8+ years of relevant experience.
• Hands-on experience integrating AI models with sensor data, control logic, and signal processing pipelines to improve the accuracy, performance, and predictive capabilities of physical hardware systems.
• Strong proficiency in Python and popular ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost).
• Hands-on experience of data preprocessing, feature engineering, and model evaluation.
• Solid knowledge of statistics, predictive modeling, and experimental methods
• Experience working with cloud-based ML platforms (AWS Sagemaker, GCP Vertex AI, or Azure ML) and containerization (Docker or similar)
• Familiarity with software engineering best practices (version control, testing, CI/CD)

Nice To Haves

• 10+ years experience in production level firmware or software engineering for real-time systems, or equivalent product development experience
• Experience developing ML-powered product features (e.g. Predictive maintenance, personalization, recommendations).
• Knowledge of A/B testing, experimentation frameworks, and data-driven product iteration.
• Understanding of MLOps principles and tools for scalable model deployment and monitoring.
• Excellent communication and collaboration skills; ability to translate complex models into actionable insights.

Responsibilities

• Design, develop, and deploy machine learning models and algorithms that solve core product challenges (e.g., recommendations, predictions, personalization, automation) with real-time instrumentation.
• Analyze and interpret large datasets generated by software systems, identifying patterns, anomalies, and behavioral trends.
• Design, train, and validate Machine Learning models for prediction, classification, clustering, and anomaly detection
• Collaborate with cross-functional teams (engineering, product, design) to integrate ML models into production systems.
• Prototype and experiment with new model architectures and data sources to continuously improve product performance.
• Monitor and evaluate model performance in production, ensuring robustness, fairness, and scalability.
• Communicate insights and model results clearly to both technical and non-technical audiences.
• Contribute to data infrastructure and pipelines that support model training, testing, and deployment.
• Stay current on advances in machine learning, AI, and data engineering — and help bring new ideas into production.
• Excellent communication skills; translating data and communicating it to internal experts, making critical decisions in collaboration with other disciplines.

Benefits

• Health coverage: medical, dental, vision, fsa, onsite clinic (CO employees), life insurance
• 401(k) retirement plan with company match
• Vacation, holiday, and leave policies
• Tuition reimbursement, Employee recognition programs, Employee assistance programs

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees