Machine Learning Engineer

We are looking for an experienced Machine Learning Engineer Consultant to design, build, validate, and deploy quantitative models that support AI-enabled business workflows. This role is ideal for someone who enjoys solving complex numerical, forecasting, optimization, pattern detection, and risk-scoring challenges. The consultant will build standalone model capabilities that can be exposed as callable tools for AI agents, helping address the types of numerical and combinatorial reasoning tasks that language models may not reliably perform on their own. This is a strong opportunity for a hands-on ML professional who can work with messy enterprise data, build explainable models, support audit-ready workflows, and communicate model behavior clearly to both technical and business stakeholders.

What You’ll Do

• Design and build standalone quantitative models for forecasting, optimization, scoring, pattern detection, and relationship analysis
• Develop model components with clear inputs, outputs, assumptions, performance expectations, and testing criteria
• Build and maintain data pipelines that combine historical data, vendor inputs, market signals, trend data, and other enterprise data sources
• Prepare model-ready features from incomplete, inconsistent, or unreliable real-world data
• Develop backtesting and evaluation frameworks to compare model outputs against historical outcomes before deployment
• Validate model performance, accuracy, reliability, confidence ranges, and known limitations
• Implement model monitoring for drift, input quality, accuracy changes, and data-source reliability over time
• Define retraining, reweighting, or escalation triggers when models or inputs become less reliable
• Build ensemble, weighting, or scoring approaches across models and data sources
• Create graceful degradation logic for missing, incomplete, or unreliable inputs
• Register models in a governed model registry with clear versioning, explainability, and audit support
• Define and document callable model interfaces, including inputs, outputs, latency, confidence bounds, and performance characteristics
• Partner with business analysts to ensure model logic reflects real business rules, exceptions, and edge cases
• Collaborate with AI platform and governance teams to ensure models integrate cleanly with downstream systems and tool contracts
• Document model design, assumptions, decision logic, limitations, and validation results for client, stakeholder, and audit review
• Support user acceptance testing by explaining model behavior, outputs, and tradeoffs in plain language

• 4 or more years of experience building and deploying machine learning models in production environments
• Strong Python skills
• Experience with common ML and data tools such as pandas, scikit-learn, or similar frameworks
• Experience with time-series forecasting, weighted scoring models, ensemble models, optimization, recommendation, or risk-scoring models
• Experience applying quantitative models to enterprise business problems
• Comfortable working with messy real-world data, including missing values, inconsistent history, and unreliable third-party inputs
• Experience building explainable models or decision logic for human-reviewed, regulated, or audited workflows
• Familiarity with integrating ML outputs into downstream business systems through APIs, event-driven pipelines, or similar methods
• Strong communication skills with the ability to explain model behavior to non-technical stakeholders
• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related field

Preferred Background

• Experience with forecasting, optimization, or recommendation models in an enterprise setting
• Experience working with governed model platforms, model registries, explainability requirements, or audit-ready ML workflows
• Experience dynamically adjusting model weights based on observed data-source reliability
• Background working with platform, governance, or AI teams building human-in-the-loop AI systems
• Experience designing model outputs that can be consumed by agents, business applications, or decision-support tools
• Consulting experience or experience working with client-facing technical teams

• Competitive compensation based on experience
• Comprehensive benefits package, if applicable
• Flexible work arrangements
• Professional growth and career advancement opportunities
• Certification and learning support
• Collaborative, inclusive, and mission-driven team environment
• Opportunity to work on advanced AI, ML, and enterprise transformation initiatives