Data Scientist

Ford Pro is a new global business within Ford committed to commercial customer productivity. Ford Pro delivers a work-ready suite of vehicles, accessories, and services for virtually every vocation, backed by technology and engineered for uptime. A true one-stop shop, offering a full portfolio of electrified and internal combustion vehicles designed to integrate seamlessly with the Ford Pro ecosystem, helping customers' businesses thrive today and into the new era of electrification.

This is an exciting opportunity to be part of a team that will be instrumental in crafting the future of how we do business, with the commercial customer at the center of everything we do.

As the AI Engineer, you should have a strong technical background and demonstrated experience in Data Science, MLOps and AI/ML Engineering. You will be responsible for designing, documenting, and maintaining clear processes for AI project lifecycles, including data acquisition, model development, deployment, and monitoring. Your responsibilities will include the delivery of analytical models and solutions as well as use your business acumen, knowledge of data, and AI to support key products within Pro Tech that focus on improving customer experience, increasing revenue, and improving efficiency across the business.

**The minimum requirements we seek**:

- Bachelor’s Degree in related field (e.g. Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, IT)
- 3 to 7 years of experience using AI/ML, data science software (e.g., Python-based tools)
- Experience in leading Data Science teams.
- Experience acting as the senior technical lead helping facilitate analytical/technical discussions on solution tradeoffs for new feature implementations.
- Experience using Cloud Platforms and AI Platforms.
- Experience using Gen AI technologies.

**Our preferred requirements**:

- Master’s or PhD Degree in related field (e.g., Data Science, Predictive Analytics, Machine Learning, Statistics, Applied Mathematics, Computer Science)
- Experience leading and managing teams.
- Expert level of Advanced and Predictive Analytical Methods e.g., Simulation, Design of Experiments, Genetic Algorithms, Ensemble Methods, Naïve Bayes, Neural Networks, regression, image processing, natural language processing,
- Working knowledge of GCP.
- Expertise in open-source data science technologies such as Python, R, Spark, SQL.

**Strategic Thinking & Leadership**
- Collaborate with business customers to understand business challenges and develop strategies for solving them using AI/ML and advanced analytics.
- Help define and communicate product vision and delivery with key stakeholders.
- Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.

**Technical Leadership**
- Support technical reviews of analytical methods & AI solutions.
- Support the prioritization of advanced analytic research needed to advance the product’s capabilities.
- Hands on experience of developing analytical solutions.
- Contribute to and grow our AI Engineering practice

**Delivery Focus**
- Drive AI products and services end-to-end in partnership with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.Influence product direction through clear and compelling presentations to leadership.

**Team Development**
- Contribute towards advancing the AI Engineering and Data Science discipline in Ford Pro through the Community of Practice, including but not limited to driving data best practices, improving analytical processes, scaling knowledge and tools, and mentoring other data scientists.
- Promote and facilitate learning and development needs of team.Support team members as part of product delivery model.

**Anchor Role**
- Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow.
- Strong understanding of machine learning algorithms, techniques, and frameworks, including deep learning, neural networks, and ensemble methods.
- Experience with building and training machine learning models using tools like TensorFlow, Keras, or PyTorch.
- Familiarity with cloud-based data storage and processing technologies for handling large datasets efficiently.
- Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment.
- Proficiency in programming languages such as Python for data manipulation, analysis, and model development.
- Experience with version control systems like Git for managing code repositories and collaboration.
- Understanding of containerization technologies like Docker for packaging machine learning models and deploying them in production.
- Strong problem-solving skills, analytical thinking, and the ability to communicate complex technical