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# Lead Data Scientist

- **Company:** Black Rock Groups
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Databases, Data as a Services, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Visualization, Data Warehousing, Relational Databases, Database Queries, DevOps, Document-Oriented Databases, Apache Hadoop, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, MySQL, NoSQL, Systems Development Life Cycle, Power BI, Tensorflow, Tableau (Software), Workflow Management Systems, Google Cloud, Pytorch, System Availability, Apache Spark, Containerization, Scikit Learn, Kubernetes, Information Technology, Cassandra, Apache Kafka, Data Management, Looker Analytics, Data Pipelines, Docker, Programming Languages
- **Published:** November 13, 2026
- **Apply:** https://www.careerbuilder.com/job-details/lead-data-scientist-tx--85146472-be10-4b2d-99cf-88d79c765d0f

## About the Role

Data Science, Data Management, Data Science, Machine Learning, Database Extract Transform and Load (ETL), Artificial Intelligence (AI), Data Modeling, Team Player, Scalable System Development, Data Warehousing, We are seeking a highly skilled Data Engineer with a strong background in AI/ML Engineering to join our dynamic team in Plano, Texas. The ideal candidate will have 7 10 years of experience in data engineering, with a proven track record of designing and implementing data pipelines and architectures that support advanced analytics and machine learning initiatives. This role requires a deep understanding of data modeling, ETL processes, and the ability to work collaboratively with data scientists and other stakeholders to drive data driven decision making., * Strong expertise in AI/ML Engineering, including experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit learn.

 * Proficient in programming languages such as Python, Java, or Scala.
 * Extensive experience with data modeling, ETL processes, and data warehousing solutions.
 * Hands on experience with big data technologies such as Hadoop, Spark, or Kafka.
 * Strong SQL skills and experience with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).
 * Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services.

Preferred Skills:

 * Experience with data visualization tools such as Tableau, Power BI, or Looker.
 * Knowledge of data governance and data privacy regulations.
 * Experience with containerization technologies such as Docker and orchestration tools like Kubernetes.
 * Familiarity with Agile methodologies and DevOps practices., * Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; Master's degree preferred.
 * 7 10 years of experience in data engineering or a related field, with a strong focus on AI/ML Engineering.
 * Proven ability to work in a fast paced environment and manage multiple priorities.
 * Excellent problem solving skills and attention to detail.
 * Strong communication and interpersonal skills, with the ability to work collaboratively in a team environment.

If you are passionate about data engineering and have a strong foundation in AI/ML Engineering, we encourage you to apply and join our innovative team!

## Description

* Design, develop, and maintain scalable data pipelines and architectures to support AI/ML applications.
 * Collaborate with data scientists to understand data requirements and ensure data availability for machine learning models.
 * Implement ETL processes to extract, transform, and load data from various sources into data warehouses and lakes.
 * Optimize data storage and retrieval processes for performance and efficiency.
 * Monitor and troubleshoot data pipeline performance and reliability.
 * Ensure data quality and integrity through rigorous testing and validation processes.
 * Stay updated with the latest trends and technologies in data engineering and AI/ML.
 * Document data engineering processes and maintain clear communication with cross functional teams.