Data Scientist at Ford Global Career Site

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

0.0

Posted On

19 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Data Sourcing, SQL, Exploratory Data Analysis, Statistical Analysis, Machine Learning, Data Visualization, Cross-Functional Collaboration, Documentation, Data Governance, Natural Language Processing, Large Language Models, Python, Data Manipulation, Problem-Solving, Communication, Ethical AI

Industry

Motor Vehicle Manufacturing

Description

1. 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. 2. Exploratory Data Analysis (EDA): Conduct thorough exploratory data analysis to understand data structures, identify trends, anomalies, and potential data quality issues. 3. 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). 4. 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. 5. 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. 6. 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. 7. 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. 8. 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. 1. 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. 2. 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. 3. Automated Report Generation: Summarizing daily production reports, quality summaries, or shift handover notes. 4. 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. 5. 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. 6. 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): Familiarity with R. Data Manipulation & Analysis Libraries: Python: Pandas, NumPy for data cleaning, transformation, and numerical operations. Statistical & Machine Learning Fundamentals: Solid understanding of core statistical concepts (e.g., hypothesis testing, probability, regression analysis). Experience with classical machine learning algorithms (e.g., linear/logistic regression, decision trees, random forests, clustering, SVMs) and their application using libraries like Scikit-learn. Understanding of model evaluation metrics and cross-validation techniques. Database Skills: Strong SQL proficiency for querying, manipulating, and extracting data from relational databases. Familiarity with database concepts and data warehousing principles. Problem-Solving & Analytical Thinking: Demonstrated ability to translate complex business problems into tractable data science questions. Strong analytical skills to identify patterns, draw conclusions, and propose data-driven solutions. Communication & Storytelling: Ability to clearly articulate complex technical concepts and findings to both technical and non-technical stakeholders. Experience creating compelling visualizations and presentations to convey insights. Understanding of LLM Concepts: Fundamental knowledge of what Large Language Models (LLMs) are, their underlying architectures (e.g., Transformers at a high level), and their core capabilities (text generation, summarization, translation, question answering, classification). Awareness of LLM limitations, such as hallucination, bias, and computational costs. Practical LLM Application: Hands-on experience with Prompt Engineering: The ability to craft effective and nuanced prompts to guide LLMs towards desired outputs for specific tasks. Experience utilizing LLMs via APIs: Working with commercial LLM APIs (e.g., OpenAI's GPT series, Google's Gemini API, Anthropic's Claude API) or open-source LLMs through frameworks. Natural Language Processing (NLP) Fundamentals: Basic understanding of core NLP concepts like tokenization, embeddings, text preprocessing (cleaning, normalization), and common NLP tasks (e.g., sentiment analysis, named entity recognition, text classification). This is crucial for preparing data for LLMs and interpreting their outputs. LLM Frameworks/Libraries (Practical Use): Familiarity with and practical experience using libraries like Hugging Face Transformers for accessing and working with pre-trained models. Experience with LangChain or LlamaIndex for building more complex LLM applications (e.g., RAG pipelines, agents). Fine-tuning & Adaptation (Conceptual & Practical): Understanding the concept of fine-tuning pre-trained LLMs on smaller, domain-specific datasets to improve performance for particular tasks. Some practical experience (even if limited) with fine-tuning techniques or using specialized tools for model adaptation. Evaluation of LLM Outputs: Knowledge of metrics and methods for evaluating the quality and relevance of LLM-generated text or classifications for specific use cases. Ethical AI & Responsible LLM Use: Awareness of ethical considerations related to LLMs, including data privacy, fairness, potential misuse, and transparency. You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply! As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder or all of the above? No matter what you choose, we offer a work life that works for you, including: Immediate medical, dental, vision and prescription drug coverage Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up childcare and more Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more Vehicle discount program for employees and family members and management leases Tuition assistance Established and active employee resource groups Paid time off for individual and team community service A generous schedule of paid holidays, including the week between Christmas and New Year's Day Paid time off and the option to purchase additional vacation time. This position is a salary grade 8. For more information on salary and benefits, click here: https://fordcareers.co/GSRSP4 Visa sponsorship is available for this position. Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660. LI-Onsite #LI-SJ2

Responsibilities

The Data Scientist will identify, access, and integrate data from various manufacturing databases and systems, and conduct exploratory data analysis to understand data structures and identify trends. They will apply statistical methods and machine learning algorithms to analyze complex manufacturing processes and generate actionable business insights.