Data Science Engineer

hace 1 semana

Naucalpan, México Ford Motor Company A tiempo completo

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.

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