Data Scientist at Ford Global Career Site

, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

31 May, 26

Salary

0.0

Posted On

02 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Big Data, Statistics, Econometrics, Optimization, Python, Generative AI (GenAI), Natural Language Processing (NLP), Natural Language Generation (NLG), Generative Adversarial Networks (GANs), TensorFlow, PyTorch, Scikit-learn, GCP, Data Mining, Text Mining

Industry

Motor Vehicle Manufacturing

Description

GDIA Mission and Scope 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, Warranty Analytics, and exploring AI applications within Engineering Design and CAD modeling. Potential Candidates Potential candidates should have hands-on experience in applying first principles methods, machine learning, data mining, and text mining techniques to build analytics prototypes that work on massive datasets. Candidate should have proficiency in Python, expertise in Generative AI (GenAI), Natural Language Processing (NLP), & Natural Language Generation (NLG). Experience with generative models such as Generative Adversarial Networks (GANs) for applications like design exploration or CAD model manipulation is a strong plus. Experience of developing commonly used predictive models like - Linear Regression, Decision Trees, SVM, KNN, Gradient Boost, Random Forest etc. Proficiency in cloud platforms like GCP, including experience with deploying AI applications. Solid programming skills in Python and experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Proven track record of delivering successful AI projects and driving business impact. Strong understanding of text pre-processing & normalization techniques such as tokenization. Excellent communication, presentation, and documentation skills. Strong problem-solving abilities and a proactive attitude towards learning and adopting new technologies.

Responsibilities

The role involves developing scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization across various domains like Connected Vehicle and Manufacturing. The goal is to drive evidence-based decision making by providing insights derived from data analysis.