Quorum Business Solutions-posted 29 days ago
Full-time • Mid Level
Hybrid • Houston, TX
Professional, Scientific, and Technical Services

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.

  • 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.
  • 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 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 SageMaker, or GCP Vertex AI
  • Proficiency with Python and ML frameworks TensorFlow, PyTorch, scikit-learn)
  • Comfortable with MLOps practices: versioning, automated retraining, production monitoring
  • Experience with exploratory data analysis, feature engineering, and model experimentation
  • Experience establishing data science practices and teams from scratch
  • Experience defining when to use custom models vs traditional algorithms vs LLM-based solutions
  • Background in time series forecasting and anomaly detection
  • Familiarity with oil and gas operations or industrial IoT domains Experience with predictive maintenance and equipment reliability models Knowledge of production optimization and operational efficiency use cases Experience with LLM fine-tuning and evaluation
  • Knowledge of RAG architectures and vector databases
  • Experience with Databricks, Unity Catalog, or similar platforms Background in feature engineering and data pipeline development Expertise in A/B testing and model performance evaluation
  • Experience with cost optimization for ML workloads
  • Familiarity with both traditional ML and deep learning approaches
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