Machine Learning Platform Lead

Location:

Houston, Texas or Dallas, Texas or Calgary, Alberta, Canada

Model of Work:
Hybrid

Are you excited by challenges? Do you enjoy working in a fast‑paced, international and dynamic environment? Then now is the time to join Quorum Software, a rapidly growing company and industry leader in oil & gas transformation.

Quorum Software is the world's largest provider of digital technology focused solely on business workflows that empower the next evolution of energy. From emerging companies to supermajors, throughout every region of the globe, customers rely on Quorum's proven innovation and unmatched global expertise to streamline business operations and make data‑driven decisions that optimize profitability and growth. Our industry‑leading solutions are transforming energy companies across the entire value chain, helping visionary leaders evolve their organizations into modern energy companies.

Overview
We're seeking a Machine Learning (ML) Platform Lead to establish Quorum's machine learning capability. This role combines hands‑on technical architecture with team building and strategic direction. You'll define how we do ML at Quorum while shipping actual models.

The ideal candidate brings deep experience in both model development and production ML infrastructure. You'll architect our ML platform (model registry, training pipelines, deployment infrastructure, MLOps practices) while hiring and leading an ML team. You'll also establish our data science practice, defining how we identify opportunities, evaluate solutions, and measure impact across our product portfolio.

This role requires someone who can make pragmatic decisions about when ML is necessary versus when simpler solutions suffice. You'll work closely with product teams to translate business needs into technical requirements and guide investment decisions based on clear trade‑offs.

Strong communication skills and experience managing stakeholder relationships are essential. You'll coordinate with engineering leaders across multiple product lines and establish the engagement model between ML and product teams.

Responsibilities

Establish and lead Quorum’s machine learning capability, defining the overall vision, architecture, and operating model for ML across the organization.

Design and implement the ML platform, including model registry, training and deployment pipelines, and scalable MLOps practices.

Develop and ship production‑grade ML models, balancing hands‑on technical work with leadership and strategy.

Build and lead a high‑performing ML team, including hiring, mentoring, and defining team structure and processes.

Define and grow Quorum’s data science practice, setting standards for identifying opportunities, evaluating models, and measuring impact across products.

Collaborate with product and engineering teams to translate business needs into ML solutions and determine when ML is or isn’t the right approach.

Drive alignment across product lines, establishing clear engagement models and communication channels between ML, engineering, and product stakeholders.

Make pragmatic, data‑driven decisions about technical trade‑offs, investment priorities, and platform evolution.

Represent the ML function in cross‑functional planning and leadership discussions, ensuring business value and technical excellence remain aligned.

And other duties as assigned.

Requirements

3+ years in machine learning and data science, with significant production ML experience

Proven track record shipping production ML models and maintaining them at scale

Experience architecting ML infrastructure: model registries, training pipelines, deployment systems, monitoring

Strong background bridging model development and production engineering and experience leading technical teams and hiring talent

Ability to translate business problems into pragmatic ML solutions

Hands‑on experience with at least one cloud ML platform (Databricks, Azure ML, AWS Sage Maker, or GCP Vertex AI)

Proficiency with Python and ML frameworks (Tensor Flow, PyTorch, scikit‑learn)

Comfortable with MLOps practices: versioning, automated retraining, production monitoring

Experience with exploratory data analysis, feature…