Data Scientist – AI/ML Modeling
Core
Design, validate, and productize custom machine learning and deep learning models for laboratory solutions, converting scientific workflow challenges into rigorous modeling problems.
Role type
Senior IC machine-learning engineer (scientific/laboratory domain)
Builds
Production ML/DL models for laboratory productivity use cases (chromatography, mass spectrometry, image analysis, anomaly detection)
Domain
Life sciences / Analytical chemistry / Laboratory instrumentation
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, model selection, training strategy, validation design, optimization, evaluation metrics, error analysis, statistics, applied mathematics, signal processing, time-series analysis, computer vision, chemometrics
Preferred skills
Domain knowledge in chromatography, mass spectrometry, spectral analysis, scientific imaging, model robustness, explainability, uncertainty handling, model serving, APIs, containerization, CI/CD integration
Technologies
PyTorch, TensorFlow, scikit-learn, NumPy, Pandas
Responsibilities
Convert AI product requirements and scientific use cases into clear ML problem statements and experimental plans; Design, train, validate, and optimize custom ML/DL models for laboratory data; Select and adapt architectures for scientific data (convolutional, segmentation, temporal, graph-based, physics-informed); Build reproducible modeling workflows covering data curation, labeling, feature engineering, and documentation; Collaborate with domain experts to design experiments and generate high-quality datasets; Define performance metrics and statistical validation approaches for scientific decision-making; Translate prototypes into product-ready capabilities by collaborating with software engineering on deployment patterns and lifecycle needs; Monitor applied AI/ML research to identify methods with practical deployment value
Seniority
Senior, hands-on IC