Minimum Qualifications
- Bachelor’s or master’s degree in data science, Computer Science, Industrial Engineering, Statistics, or a related technical field.
- 3+ years of experience developing data science solutions for operational or industrial use cases.
- Strong programming skills in Python and experience with relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
- Strong understanding and experience with data engineering fundamentals—data wrangling, pipeline orchestration, and ETL processes.
- Solid understanding of statistical modeling and machine learning algorithms.
- Proficiency with relational and distributed databases (e.g., SQL, Spark, Delta Lake) and query languages and experience working with large datasets.
- Experience with cloud platforms (e.g., GCP, AWS, Azure) and their data science services.
- Understanding of version control, testing, and CI/CD in a data science context.
- Strong communication skills and ability to explain technical solutions to cross-disciplinary audiences.
- Strong analytical and problem-solving skills and excellent data visualization skills.
Preferred Qualifications
- Experience working with manufacturing, industrial IoT, or process control systems.
- Knowledge of Data-Centric Architecture principles and experience industrializing reusable data products (features, labels, models).
- Familiarity with time series forecasting, anomaly detection, root cause analysis, or reinforcement learning in industrial settings.




- Experience with cloud-native data services (e.g., Azure Data Factory, AWS SageMaker, Databricks, or GCP Vertex AI).
- Exposure to edge computing and deploying models close to the source (e.g., plant floor or local gateways).
- Demonstrated experience building pipelines and model management systems that are robust, maintainable, and scalable.
- Experience with MLOps practices and tools.
- Knowledge of experimental design and causal inference.
- Domain knowledge in the automotive industry.
- Design and implement scalable data science solutions that turn manufacturing data into actionable insights across the factory network.
- Develop and deploy predictive models (e.g., quality prediction, anomaly detection, throughput forecasting) directly into production systems.
- Architect and implement components within the guardrails of Ford’s Data-Centric Architecture (DCA), ensuring data pipelines, features, and models are reusable, observable, and aligned to product needs.
- Translate domain and business problems into mathematical and statistical formulations using appropriate modeling techniques.
- Work closely with plant engineers and cross-functional stakeholders to validate, interpret, and continuously improve data-driven systems.
- Ensure robust data quality, governance, and lineage across systems spanning on-prem (factory) and cloud environments.
- Contribute to MLOps practices: versioning, monitoring, retraining, and automated deployment of ML models at scale.

📌 Data Science Engineer (Naucalpan de Juárez)
🏢 Ford Motor
📍 Naucalpan de Juárez