Sr AI Data Scientist

Ford Motor; Naucalpan de Juárez; Presencial; MXN 400,000 - 600,000; Jornada completa

Descripción de la vacante

A leading automobile manufacturer is looking for a skilled data analytics expert in Naucalpan de Juárez. The role involves using your data expertise to implement machine learning solutions and collaborate with business partners. Candidates should have over 5 years of experience with machine learning technologies and data analytics, as well as a strong educational background in a related field. This position offers opportunities to shape the future of transportation.

Formación

• 5+ years of experience in machine learning, data science, or related fields.
• Proficiency in feature engineering and model evaluation.
• Understanding of computer vision and AI models.

Responsabilidades

• Apply various deep learning networks and statistical techniques.
• Transform large scale data for modeling requirements.
• Interact with stakeholders to address business problems.

Conocimientos

Machine Learning

Data Analysis

Statistical Modeling

Deep Learning

Educación

Bachelor’s or Post-Graduate degree in relevant field

Herramientas

Python (Pandas, Numpy, ScikitLearn)

Pytorch

TensorFlow

Keras

Descripción del empleo

Overview

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation.

Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That’s where you can make an impact as part of our Global Data Insight & Analytics team. We are the trusted advisers that enable Ford to clearly see business conditions, customer needs, and the competitive landscape. With our support, key decision-makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision-making.

The Global Data Insights and Analytics (GDI&A) department at Ford Motor Company is looking for qualified people who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization. The goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.

Potential candidates should have excellent depth and breadth of knowledge in machine learning, data mining, and statistical modeling. They should possess the ability to translate a business problem into an analytical problem, identify the relevant data sets needed for addressing the analytical problem, recommend, implement, and validate the best suited analytical algorithm(s), and generate/deliver insights to stakeholders. Candidates are expected to regularly refer to research papers and be at the cutting-edge with respect to algorithms, tools, and techniques. The role is that of an individual contributor; however the candidate is expected to work in project teams of 2 to 3 people and interact with Business partners on regular basis.

Qualifications

• English proficiency (written and verbal).
• Bachelor’s or Post-Graduate degree in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline.
• Minimum of 5+ years of professional experience in machine learning, data science, or related fields, applying the skills listed below.
• Should have experience in feature engineering, hyper parameter tuning, model evaluation, etc.
• Strong foundation in machine learning and deep learning principles, with practical experience in several of the following areas: classification and regression, unsupervised learning (e.g., clustering, dimensionality reduction), computer vision (e.g., image classification, object detection, segmentation), Large Language Models (LLMs) (including prompt engineering, fine-tuning, and Reinforcement Learning from Human Feedback (RLHF)), AI agents and automated workflows, time series analysis (applying techniques such as recurrent neural networks (RNNs)), and generative AI models (including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models).
• Should have experience in using Pandas/Numpy/ScikitLearn, Pytorch, Tensorflow, Keras.
• (Highly Desirable): Experience with containerization (e.g., Docker), constructing REST APIs using frameworks like Flask or FastAPI, experience with message brokers (e.g., Kafka, RabbitMQ), and adherence to best software development practices (e.g., version control, testing).
• (Highly Desirable): Familiarity with cloud platforms and services, ideally including experience with Google Cloud Platform (GCP) services such as Cloud Run, BigQuery, Cloud SQL, Pub/Sub, Compute Engine, Artifact Registry, or related services from other cloud providers (e.g., AWS, Azure).

DISCLAIMER

Ford Motor Company is an Equal Opportunity Employer, as we are committed with a diverse workforce, and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran and basis of disability.

Responsibilities

• Applying various Deep learning networks, statistical techniques, explore and experiment on new models through research papers or via various frameworks.
• Understanding, transforming large scale data to usable form for modelling, filtering data with generalization for later use, Cross-validating models for the requirements.
• Recommend and justify the algorithms to implement for the problems at-hand.
• Enhance deep learning networks with multi-GPU and multi-node capabilities.
• Interact with internal stakeholders to understand the business problems.
• Applying calculus, algebra and other math to build reliable, scalable model.
• Automate algorithms in production through standardization of process and authoring best practices.
• Producing and disseminating technical and non-technical reports that detail the successes and limitations of each project.