Senior Data Scientist

Brooksource; Houston (TX); On-site; USD 199,752,000 - 228,682,000; Full time

Benefits offered by this job

Dental insurance

Health insurance

Vision insurance

401k with match

Paid time off

Job summary

Brooksource is partnering with a rapidly growing Data and AI organization within a leading energy company to build enterprise-scale AI, MLOps, and analytics capabilities. This onsite, contract-to-hire opportunity sits in Houston, TX, offering $70-$80 per hour and the chance to influence the organization's AI strategy while working hands-on with modern cloud platforms.

The centralized innovation team acts as the hub for Data Science, AI, and Data Engineering, delivering reusable solutions that

Qualifications

• 7+ years of experience in Data Science, ML Engineering, Data Engineering, or a related technical field.
• Proven experience leading production AI or advanced analytics initiatives in enterprise environments.
• Strong hands-on experience with Databricks and Azure Cloud; other major cloud platforms considered.
• Advanced proficiency in Python, PySpark, Spark SQL, and SQL.
• Experience building scalable ETL/ELT pipelines and modern data architectures.
• Experience deploying ML models into production using MLOps frameworks and best practices.
• Hands-on experience implementing CI/CD pipelines using Git, GitHub, Azure DevOps, or comparable technologies.
• Experience working with large-scale distributed data processing platforms.
• Strong understanding of data governance, model governance, security, and enterprise AI best practices.
• Ability to lead technical initiatives while remaining hands-on with development.
• Excellent communication skills with experience partnering across technical teams and business stakeholders.

Responsibilities

• Steer end-to-end lifecycle of ML and AI solutions within Azure and Databricks environments, from design through deployment and production readiness.
• Design scalable data science workflows that transition models from research to production.
• Collaborate with Data Engineering to build robust ETL/ELT pipelines supporting enterprise AI programs.
• Develop and optimize machine learning pipelines using Python, PySpark, Spark SQL, Databricks, and Azure services.
• Lead MLOps and AgentOps practices, including model lifecycle management, monitoring, deployment automation, and governance.
• Implement CI/CD pipelines with Git, GitHub, Azure DevOps, or equivalent tools to automate testing, deployment, and releases.
• Partner with AI Engineers to develop and productionize Generative AI, LLM, and intelligent agent solutions when applicable.
• Champion AI governance, model management, reproducibility, security, and responsible AI practices.
• Mentor junior Data Scientists and Engineers while providing technical leadership across cross-functional initiatives.
• Translate complex business problems into scalable AI and analytics solutions for enterprise reuse.
• Collaborate with business stakeholders to prioritize high-impact opportunities and deliver measurable value.
• Influence architectural decisions and contribute to the long-term roadmap for enterprise AI capabilities.

Skills

Data science

ML engineering

Data engineering

Leadership

Python

PySpark

SQL

Azure

Tools

Azure Cloud

Azure Databricks

Databricks

Python

PySpark

Spark SQL

SQL

Delta Lake

Azure Data Factory

Git

GitHub

Azure DevOps

MLflow

MLOps

CI/CD

Job description

Brooksource is partnering with a rapidly growing Data and AI organization within a leading energy company to build enterprise scale AI, MLOps, and analytics capabilities. This onsite, contract-to-hire opportunity sits in Houston, TX, offering $70-$80 per hour and the chance to influence the organization's AI strategy while working hands-on with modern cloud platforms. The centralized innovation team acts as the hub for Data Science, AI, and Data Engineering, delivering reusable solutions that accelerate value across multiple business units.

Overview

In this lead data science role, you will supervise the design and execution of AI and analytics initiatives at the enterprise level. The ideal candidate blends deep technical expertise with the ability to shape scalable, governed platforms that move AI from experimentation into production.

Team & Environment

You will report to a Data Science leadership group and serve as a technical leader on a growing enterprise AI team that supports several business units. This is a high-visibility position with substantial influence on the organization’s AI roadmap and digital transformation efforts. Preference is given to candidates located in or willing to relocate to the Houston area, with onsite work and a path toward long-term conversion to full-time.

Responsibilities

• Steer end-to-end lifecycle of ML and AI solutions within Azure and Databricks environments, from design through deployment and production readiness.
• Design scalable data science workflows that transition models from research to production.
• Collaborate with Data Engineering to build robust ETL/ELT pipelines supporting enterprise AI programs.
• Develop and optimize machine learning pipelines using Python, PySpark, Spark SQL, Databricks, and Azure services.
• Lead MLOps and AgentOps practices, including model lifecycle management, monitoring, deployment automation, and governance.
• Implement CI/CD pipelines with Git, GitHub, Azure DevOps, or equivalent tools to automate testing, deployment, and releases.
• Partner with AI Engineers to develop and productionize Generative AI, LLM, and intelligent agent solutions when applicable.
• Champion AI governance, model management, reproducibility, security, and responsible AI practices.
• Mentor junior Data Scientists and Engineers while providing technical leadership across cross-functional initiatives.
• Translate complex business problems into scalable AI and analytics solutions for enterprise reuse.
• Collaborate with business stakeholders to prioritize high-impact opportunities and deliver measurable value.
• Influence architectural decisions and contribute to the long-term roadmap for enterprise AI capabilities.

Requirements

• 7+ years of experience in Data Science, ML Engineering, Data Engineering, or a related technical field.
• Proven experience leading production AI or advanced analytics initiatives in enterprise environments.
• Strong hands-on experience with Databricks and Azure Cloud; other major cloud platforms considered.
• Advanced proficiency in Python, PySpark, Spark SQL, and SQL.
• Experience building scalable ETL/ELT pipelines and modern data architectures.
• Experience deploying ML models into production using MLOps frameworks and best practices.
• Hands-on experience implementing CI/CD pipelines using Git, GitHub, Azure DevOps, or comparable technologies.
• Experience working with large-scale distributed data processing platforms.
• Strong understanding of data governance, model governance, security, and enterprise AI best practices.
• Ability to lead technical initiatives while remaining hands-on with development.
• Excellent communication skills with experience partnering across technical teams and business stakeholders.

Technologies

• Azure Cloud, Azure Databricks
• PySpark, Spark SQL, Python, SQL
• Delta Lake, Azure Data Factory
• Git, GitHub, Azure DevOps
• MLflow, CI/CD Pipelines, MLOps / AgentOps
• Enterprise AI and Machine Learning

Benefits

• Dental insurance
• Health insurance
• Vision insurance
• 401k plan with company match
• Paid time off