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Data Scientist
Ford Motor CompanyPalo Alto, United Statesfull_timeVerifiedPosted 29 Jul 2025
About the role
- Data Sourcing & Integration:
- Identify, access, and integrate data from a multitude of internal and external car manufacturing databases and systems, including but not limited to: MES (Manufacturing Execution Systems), ERP (Enterprise Resource Planning), SCADA, PLC logs, Quality Management Systems, Supply Chain databases, sensor data, and R&D databases.
- Develop and optimize SQL queries, data connectors, and ETL/ELT processes to efficiently extract, transform, and load data from these diverse sources into analytical platforms.
- Exploratory Data Analysis (EDA): Conduct thorough exploratory data analysis to understand data structures, identify trends, anomalies, and potential data quality issues.
- Statistical Analysis & Modeling: Apply appropriate statistical methods, data mining techniques, and machine learning algorithms (where applicable) to analyze complex manufacturing processes, identify root causes of issues, predict outcomes, and uncover opportunities for optimization (e.g., defect analysis, cycle time optimization, energy consumption patterns).
- Insight Generation & Storytelling: Translate complex analytical findings into clear, concise, and actionable business insights. Develop compelling narratives around the data that resonate with non-technical stakeholders.
- Visualization & Dashboard Development:
- Ensure visualizations effectively communicate key performance indicators (KPIs), trends, and insights, enabling users to explore data and make data-driven decisions independently.
- Focus on user experience and maintainability for all developed dashboards.
- Cross-Functional Collaboration: Partner effectively with manufacturing engineers, production managers, quality control specialists, supply chain analysts, and other business stakeholders to understand their challenges, define analytical requirements, and deliver relevant data solutions.
- Documentation & Best Practices: Document data sources, data models, analytical methodologies, and dashboard specifications. Promote and adhere to best practices in data governance, data quality, and visualization.
- Continuous Improvement: Stay abreast of the latest trends in data science, business intelligence, and manufacturing analytics. Propose and implement new tools, techniques, and methodologies to enhance our analytical capabilities.
- Analyzing Unstructured Data: Manufacturing generates a vast amount of unstructured text data:
- Maintenance logs: Identifying common failure modes, predicting equipment breakdowns based on technician notes.
- Quality reports: Extracting patterns from defect descriptions, customer complaints, and warranty claims.
- Supplier documentation: Summarizing complex specifications or contracts.
- Safety incident reports: Identifying root causes and prevention strategies.
- Process documentation: Quickly finding relevant information across thousands of pages of manuals.
- Knowledge Management & Search: Building intelligent search systems or internal chatbots that allow engineers and factory workers to quickly find information in technical documents, design specifications, or past troubleshooting guides.
- Automated Report Generation: Summarizing daily production reports, quality summaries, or shift handover notes.
- Augmenting Data Collection: In some cases, LLMs could help in generating synthetic data for training other models, or in helping to structure semi-structured data.
- Human-Machine Interaction: Potentially developing natural language interfaces for factory floor systems, allowing technicians to query data or control processes using voice or text commands.
- Code Assistance: Assisting data scientists themselves with writing, debugging, or optimizing code for data pipelines and analytical models.
- Education:
- Required: Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related discipline.
- With a Master's degree must have 3-5 years' experience
- (Preferred but not strictly basic): PhD in a relevant quantitative field; 0-3 years' experience
- Programming Proficiency:
- Expert-level proficiency in Python is essential. This includes strong command of core Python libraries for data manipulation and analysis.
- (Optional but beneficial): Famili
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