Data Scientist

QODE • Texas, United States • Full-time

$120,000

per year

Machine Learning Computer Vision AI solutions Data Science Deep Learning Predictive Analytics NLP

Job Description

Role Summary

We are seeking a highly skilled Lead Data Scientist / Senior Machine Learning Engineer to join our AI/ML and Advanced Analytics team. In this role, you will drive the development and deployment of cutting-edge machine learning, deep learning, and Generative AI solutions to solve complex business problems across Verizon’s business units. You’ll collaborate with cross-functional teams to deliver intelligent systems that scale, are responsible, and deliver measurable business impact.

Key Responsibilities

Design, develop, and deploy production-grade machine learning and deep learning models for predictive analytics, NLP, and computer vision use cases.
Lead the research and application of Generative AI (LLMs, diffusion models, multimodal models) to build innovative solutions in customer service, marketing, network optimization, and operations.
Collaborate with stakeholders to identify high-impact opportunities and formulate AI-driven strategies.
Guide and mentor junior data scientists and engineers; set best practices in modeling, data engineering, and MLOps.
Design and lead end-to-end ML pipelines using tools like TensorFlow, PyTorch, scikit-learn, MLflow, and Kubernetes.
Optimize models for performance, explainability, and fairness.
Stay up to date with state-of-the-art research in ML/AI and rapidly prototype solutions to test new ideas.
Contribute to the company’s IP portfolio by publishing whitepapers or filing patents.

Preferred Qualifications

Master’s or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or related field.
7+ years of hands-on experience in machine learning, deep learning, or applied AI.
Proven experience with LLMs (e.g., GPT, LLaMA, Claude), Transformers, CNNs, RNNs, and generative models (e.g., GANs, VAEs).
Proficiency in Python, SQL, and ML libraries (TensorFlow, PyTorch, Hugging Face, etc.).
Familiarity with cloud platforms (AWS/GCP/Azure) and ML Ops tooling (Docker, Kubeflow, MLflow, Airflow).
Strong understanding of data structures, algorithms, and software design principles.
Excellent problem-solving skills, communication, and ability to translate business requirements into technical solutions.