> Markdown version of [/jobs/ext/4627808-data-scientist-machine-learning-deep-learning-agentic-ai-in-berkeley](https://www.wearedevelopers.com/jobs/ext/4627808-data-scientist-machine-learning-deep-learning-agentic-ai-in-berkeley). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist Machine Learning, Deep Learning & Agentic AI in Berkeley - **Company:** Energy Jobline - **Location:** Irving, TX, United States - **Salary:** $93,600 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Big Data, BigQuery, Business Software, Cloud Computing, Cloud Database, Data Cleansing, Information Engineering, Database Queries, Distributed Computing Environment, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Feature Engineering, Pytorch, Large Language Models, Deep Learning, Model Validation, Agentic-AI, Pyspark, Transformer Architectures, Machine Learning Operations, GPT, Data Pipelines - **Published:** November 7, 2026 - **Apply:** https://www.energyjobline.com/job/data-scientist-machine-learning-deep-learning-agentic-ai-berkeley-32178373 ## About the Role The role spans the complete model lifecycle, including data exploration, feature engineering, model development, deployment, and monitoring. In addition to strong modeling experience, candidates should have hands-on experience or the ability to contribute to agentic AI development, integrating AI agents and intelligent workflows with data science and machine learning solutions., * Python & SQL: Strong hands-on skills in production coding, complex queries, data analysis, and model development. * Machine Learning & Deep Learning: Experience developing, evaluating, deploying, and maintaining models in production environments. * PyTorch & PySpark: Hands-on experience with deep learning frameworks and distributed data processing. * Traditional Data Science: Experience with feature engineering, statistical modeling, churn modeling, propensity modeling, and predictive analytics. * Transformer Models: Knowledge of transformer-based architectures and their practical applications. * End-to-End ML Pipelines: Experience across data preparation, model training, deployment, monitoring, and optimization. * Cloud Technologies: Experience with GCP and BigQuery . * Agentic AI Development: Experience or practical knowledge of AI agents, agentic workflows, LLM integration, tool calling, and orchestration is an added requirement alongside traditional modeling expertise. ## Description We are looking for a Data Scientist with strong expertise in traditional data science, machine learning, deep learning, and agentic AI development to build, deploy, and maintain production-ready AI and ML solutions that support business objectives., * Build end-to-end data science and machine learning pipelines, including data preparation, feature engineering, model development, deployment, and monitoring. * Develop, train, evaluate, and deploy machine learning, deep learning, and transformer-based models. * Design and implement churn prediction, propensity modeling, and other business-focused predictive models. * Write, optimize, and maintain production-quality Python and SQL for day-to-day queries, data analysis, and model development. * Process and analyze large-scale datasets using PySpark and develop deep learning solutions using PyTorch. * Leverage GCP and BigQuery to build, deploy, and manage cloud-based data science and machine learning solutions. * Contribute to agentic AI development, including building AI agents, integrating tools, and developing intelligent workflows where applicable. * Integrate agentic AI capabilities with existing data pipelines, machine learning models, and business applications. * Monitor model performance, troubleshoot production issues, and continuously improve model accuracy, scalability, and reliability. * Collaborate with data engineering, software engineering, and business teams to translate requirements into production-ready models and actionable insights.