You will design, develop, and deploy end‑to‑end analytics, machine learning, and GenAI solutions, working with large‑scale structured and unstructured data. The role balances classical ML and statistical modeling with modern LLM‑based architectures, ensuring both approaches are applied where they create the most value.
Translate business objectives into well‑defined analytical and mathematical problem statements
Design, implement, train, tune, validate, and monitor predictive and prescriptive models
Apply machine learning techniques such as regression, classification, clustering, ensemble methods, deep learning, and time‑series analysis
Analyze large, high‑dimensional datasets to identify patterns, drivers, anomalies, and trends
Develop scalable analytics pipelines and reusable modelling frameworks suitable for automation and production deployment
Design and implement Generative AI and LLM‑based solutions, including NLP, NLG, and semantic search
Build and deploy Retrieval Augmented Generation (RAG) pipelines for enterprise knowledge and data extraction use cases
Fine‑tune, evaluate, and monitor Large Language Models for business applications
Apply text preprocessing, tokenization, embedding generation, prompt engineering, and LLM orchestration techniques
Leverage modern GenAI frameworks and libraries (e.g., Transformers, LangChain‑based workflows) to build scalable AI applications.
Build end‑to‑end system designs covering data ingestion, feature engineering, modeling, inference, and user‑facing outputs
Partner with IT and platform teams to deploy analytics and AI solutions on cloud infrastructure
Work with cross‑functional stakeholders to communicate insights, model results, and recommendations clearly
Explore new data sources, tools, and technologies to continuously enhance analytics and AI capabilities
Package findings into clear documentation, dashboards, and executive‑level presentations
Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Applied Mathematics, Operations Research, or related quantitative field
Strong hands‑on experience in Python for data analysis, machine learning, and AI development
Experience with common ML and analytics libraries (e.g., NumPy, Pandas, scikit‑learn, TensorFlow, PyTorch)
Experience with SQL and working with relational and large‑scale datasets
Experience building analytical models and communicating insights to business stakeholders
Master’s or Ph.D. in a quantitative or engineering discipline
5+ years of experience applying machine learning, advanced analytics, and AI techniques in real‑world settings
Strong experience with Generative AI, LLMs, NLP, and RAG architectures
Experience with cloud platforms, especially Google Cloud Platform (GCP), including Vertex AI, BigQuery, and related services
Experience working with large‑scale data ecosystems (e.g., NoSQL databases, Hadoop ecosystem)
Proficiency with version control (Git) and collaborative development practices
Strong mathematical foundations in probability, linear algebra, optimization, and numerical methods
Excellent problem‑solving, communication, and data storytelling skills
Ability to work independently while collaborating effectively with cross‑functional teams
Comfortable operating in fast‑paced, ambiguous, and evolving problem spaces.
Job description provided by Ford.
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