Data Scientist

hace 2 meses

Naucalpan, México Ford Motor Company A tiempo completo

Ford Motor Company's GDIA organization is seeking a skilled data scientist to analyze Ford's quality data and customer feedback to identify areas for improvement. In this role, you will work closely with cross-functional teams including quality engineering, manufacturing, and customer care to guide data-driven decisions around vehicle quality and customer satisfaction.

**Key Responsibilities**:

- Analyze Ford's warranty claims data to identify defect trends in vehicle components/systems, common quality issues, and opportunities to reduce future warranty costs.
- Mine voice of the customer data sources (surveys, social media, etc.) to surface top customer pain points around quality and product reliability.
- Continuously improve the efficiency and accuracy of existing machine learning models.
- Develop scalable model architectures for fast and reliable model retraining purposes.
- Document and communicate findings and insights to stakeholders in a clear and concise manner.

**Minimum Requirements**:

- Master’s degree in computer science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Minimum of two years of experience in Python programming language.
- 1+ years’ experience in SQL programming language and relational databases.
- 2+ years of experience with data analysis and visualization Python packages such as Pandas, SciPy, Seaborn, etc.
- 2+ years of experience with supervised machine learning algorithms and at least one of the following popular Machine learning frameworks: Scikit-learn, Pytorch, TensorFlow, and XGBoost.

**Preferred Qualifications**:

- Ph.D. degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field is preferred.
- Experience with Git and GitHub for version control and collaboration.
- Experience with Google Cloud Platform for data processing and machine learning tasks.
- Solid understanding of unsupervised machine learning algorithms such as isolation forests, one-class SVM, autoencoders, etc.
- Familiarity with feature engineering concepts, Fourier transform, and statistical feature extraction and selection.
- Excellent problem solving, communication, and data presentation skills.

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