Software Technology Inc.
Senior Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Machine Learning Engineer on a contract for "length" with a pay rate of "amount". Key skills include Python, PySpark, and MLflow on Databricks. Experience in anomaly detection and production model deployment is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 27, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#MLflow #Anomaly Detection #Data Science #ML (Machine Learning) #PySpark #Monitoring #Python #Spark (Apache Spark) #Databricks
Role description
Key Responsibilities
–Design and deploy anomaly detection models for numerical, categorical, and time-series data
–Implement statistical drift monitoring across pipeline runs and data partitions
–Build ML-based completeness prediction and consistency check models
–Integrate ML DQ signals into the broader DQ alerting framework
–Monitor model performance, retrain on new data patterns, and manage model lifecycle
–Document model behaviour and communicate anomaly signals to the DQ team
Requirements
–Data science or ML engineering, with production model experience
–Proficient in Python, PySpark, and MLflow on Databricks
–Experience with anomaly detection, statistical process control, or data drift frameworks
–Familiarity with feature stores and MLOps practices
–Ability to explain model outputs to non-technical stakeholders
Key Responsibilities
–Design and deploy anomaly detection models for numerical, categorical, and time-series data
–Implement statistical drift monitoring across pipeline runs and data partitions
–Build ML-based completeness prediction and consistency check models
–Integrate ML DQ signals into the broader DQ alerting framework
–Monitor model performance, retrain on new data patterns, and manage model lifecycle
–Document model behaviour and communicate anomaly signals to the DQ team
Requirements
–Data science or ML engineering, with production model experience
–Proficient in Python, PySpark, and MLflow on Databricks
–Experience with anomaly detection, statistical process control, or data drift frameworks
–Familiarity with feature stores and MLOps practices
–Ability to explain model outputs to non-technical stakeholders
