Machine Learning Engineer Consultant
New
T
Total SuccessMachine Learning
Remote for US based candidates onlyFull-TimeSenior
Salary120,000 - 140,000 USD per year
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Job Details
- Experience
- 4 or more years of experience
- Required Skills
- PythonMachine LearningData sciencePandasscikit-learn
Requirements
- 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
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related field
Responsibilities
- 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
- 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
- Document model design, assumptions, decision logic, limitations, and validation results for client, stakeholder, and audit review
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