Data Scientist – AI/ML Modeling

Location: Shanghai, China

Company

hirify.global develops laboratory solutions and scientific instrumentation capabilities supported by AI-enabled data analysis and workflow intelligence.

What you will do

• Translate scientific workflows, customer pain points, and AI product requirements into ML problem statements, objectives, metrics, and experimental plans.
• Design, train, validate, and optimize custom machine learning and deep learning models for chromatography, mass spectrometry, image analysis, anomaly detection, and model-assisted review.
• Select architectures such as convolutional, segmentation, temporal, graph-based, multimodal, and physics-informed models for scientific data.
• Build reproducible workflows covering data curation, labeling, ground-truth definition, feature engineering, quality checks, training, evaluation, and documentation.
• Define statistical validation methods, acceptance criteria, and evidence packages for scientific and product decisions.
• Collaborate with product, software engineering, application science, quality, and field teams to move prototypes into product-ready interfaces, APIs, inference workflows, and deployment patterns.

Requirements

• Master’s degree or Ph.D. preferred in data science, machine learning, statistics, computer science, applied mathematics, bioinformatics, chemometrics, analytical chemistry, life sciences, or a related quantitative discipline.
• At least 5 years of relevant industry or applied research experience in machine learning, deep learning, data science, or scientific computing.
• Strong hands-on Python experience with ML and scientific computing libraries such as PyTorch, TensorFlow, scikit-learn, NumPy, and Pandas.
• Strong understanding of model selection, training strategies, validation design, optimization, evaluation metrics, and error analysis.
• Quantitative expertise in statistics, applied mathematics, optimization, signal processing, time-series analysis, computer vision, chemometrics, or related scientific-data methods.
• Fluent English and strong communication skills for technical documentation, experiment reviews, and collaboration with global stakeholders.

Nice to have

• Experience with laboratory, life science, analytical instrumentation, scientific workflow, or customer-facing product environments.
• Exposure to chromatography, mass spectrometry, peak integration, peak detection, baseline correction, spectral analysis, scientific imaging, or instrument-generated signals.
• Experience improving robustness, explainability, uncertainty handling, and performance consistency across real-world scientific datasets.
• Experience with inference design, model serving, APIs, containerization, CI/CD integration, monitoring, or lifecycle management.
• Exposure to LLMs, foundation models, or AI-assisted software development tools.

Culture & Benefits

• Full-time weekly schedule with a daytime shift.
• Occasional travel may be required.
• Work focuses on reliable, explainable, reproducible, and scientifically valid AI capabilities.
• Compensation and benefits vary by role level, work location, skills, experience, and education or training.