Data Scientist

In Global Data Insight & Analytics (GDI&A), we harness the power of data and artificial intelligence to navigate Ford Motor Company through the disruptiveness of the information age. We're a team of innovators who strive to realize the enterprise's goals, reveal hidden opportunities, and achieve data superiority.

We are looking for a seasoned Data Scientist with 3+ years of experience in AI/ML, preferably with some experience in Generative AI. You're a humble and collaborative individual who thrives in a fast-paced environment. You're passionate about developing and delivering data analytics and machine learning models that drive business impact.

• Develop a strong understanding of business problems and translate them into practical AI, Generative AI, and machine-learning solutions.
• Design, build, and maintain end-to-end AI/ML pipelines, from data ingestion and preprocessing through model development, evaluation, deployment, and monitoring.
• Develop analytics and AI solutions, including classification, time-series forecasting, LLM applications, and retrieval-augmented generation (RAG).
• Build LLM-powered applications using Python, applying prompt engineering and structured outputs to support reliable, task-appropriate results.
• Develop RAG workflows, including document ingestion, chunking, embeddings, vector search, and integration of retrieved content into LLM responses.
• Evaluate and validate machine-learning and LLM solutions using appropriate methods and metrics, such as precision, recall, F1, ROC-AUC, and RMSE.
• Write clean, efficient, and well-documented Python code for data manipulation, feature engineering, model development, and AI application integration.
• Use SQL to extract, transform, and prepare data from relational databases and other data sources.
• Collaborate with data engineers and other partners to identify and assess data sources, support reliable data infrastructure, and turn data into actionable business outcomes.
• Monitor and maintain deployed AI/ML solutions, addressing performance, reliability, quality, and model or response degradation.
• Analyze complex datasets to identify trends, patterns, and insights that inform model development and business decisions.
• Communicate technical concepts, evaluation results, and recommendations effectively to technical and non-technical stakeholders.
• Take ownership of projects and deliver outcomes independently, seeking guidance when needed.
• Contribute to a collaborative team environment and show respect for colleagues and their contributions.
• Stay current with relevant developments in AI, machine learning, LLMs, and data science, and assess their potential value for business use.
  
  

• Master's degree (M.S.) in Data Science, Computer Science, Business Analytics, Machine Learning, Statistics, or a related quantitative field.
• 3+ years of experience in AI/ML, with proven experience developing and deploying machine learning models in a production environment.
• Strong proficiency in Python for data science and machine learning.
• Expertise in SQL for data querying, manipulation, and database interaction.
• Strong skills in data acquisition, algorithm design, and model development and refinement.
• Experience with big data technologies, cloud-based data platforms (e.g., GCP, AWS), and business intelligence tools (e.g., Power BI, Streamlit, Plotly Dash).
• Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Excellent oral, written, and interpersonal communication skills.
  
  

• Ph.D. in Data Science, Computer Science, Business Analytics, Machine Learning, Statistics, or a related quantitative field.
• 5+ years of experience in data science and analysis, including leadership or mentoring roles.
• Significant experience in Generative AI, including a strong understanding of ML frameworks, algorithms, and practical implementation.
• Experience in specialized areas such as Natural Language Processing (NLP), deep learning (e.g., TensorFlow, PyTorch), or recommendation systems.
• Experience with building and managing data pipelines, including familiarity with orchestration tools (e.g., Apache Airflow, Kubeflow) or DBT.
• Certifications in Google Cloud Platform (GCP) or other cloud platforms.
• Experience with agile development methodologies and version control systems (e.g., Git).
• A record of publications or presentations in recognized journals or conferences.