Machine Learning Engineer
1 day ago
Additionally, experience integrating machine learning models with existing software suites is highly preferred.
**Key Responsibilities**:
- ** Data Wrangling and Interpretation**:
- Collect, preprocess, and clean spectral data from various sources.
- Interpret complex datasets to extract meaningful insights and patterns.
- ** Data Visualization and Interpretation**:
- Create compelling visualizations to communicate data insights and model results to stakeholders.
- Develop dashboards and reports to monitor model performance and data trends.
- ** Model Development**:
- Design, develop, and implement machine learning models specifically tailored for spectral data analysis.
- Collaborate with domain experts to incorporate chemical knowledge into model building.
- ** Model Validation and Testing**:
- Validate and test existing machine learning models to ensure accuracy and reliability.
- Perform rigorous testing and evaluation to improve model performance.
- ** Model Deployment**:
- Implement and manage processes for deploying machine learning models into production environments.
- Ensure models are scalable, maintainable, and integrated seamlessly with existing systems.
- ** Software Integration**:
- Integrate machine learning models with our proprietary software suite.
- Collaborate with software development teams to ensure smooth integration and functionality.
**Qualifications**:
- Bachelor’s or Master’s degree in Computer Engineering, Computer Science, Chemistry, or a related field.
- Minimum of 2 years of hands-on experience in machine learning, with a focus on chemometrics and spectral data analysis.
- Proficiency in programming languages such as Python, R, MATLAB, Java, and C.
- Proven experience in machine learning, with a focus on chemometrics and spectral data analysis.
- Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, scikit-learn, PyTorch).
- Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
- Familiarity with model deployment and MLOps practices.
- Excellent problem-solving skills and attention to detail.
- Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
**Preferred Qualifications**:
- Experience with chemometric techniques such as PCA, PLS, and other multivariate analysis methods.
- Knowledge of cloud platforms and services (e.g., AWS, Azure, Google Cloud).
- Experience in developing and deploying machine learning models in a production environment.
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