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Data Scientist (Remote)

icon building Company : Codvo.ai
icon briefcase Job Type : Full Time

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Job Description - Data Scientist (Remote)



Data Scientist 

 

About Us: 

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.


Role Summary 

Model development, training pipeline, and analytics backend. Works in close coordination with

the on-site Data Scientist — the on-site person provides site context and validation feedback,

the offshore person implements model improvements, retraining logic, and drift detection.

 

Responsibilities 

Model Development & Training

  • Maintain and improve the physics-based simulation engine — 19 equipment families,
  • 64+ fault signatures, first-principles governing equations
  • Run model training pipelines — dataset generation, feature engineering, model fitting, hyperparameter tuning, MLflow experiment tracking
  • Implement model retraining triggers — drift detection (PSI-based), accuracy degradation monitoring, scheduled recalibration
  • Build and maintain the champion/challenger evaluation framework — shadow scoring, A/B testing, promotion guardrails
  • Develop new fault signatures as customer feedback identifies gaps

Analytics & Calibration

  • Implement probability calibration — Platt scaling, isotonic regression, ECE monitoring
  • Build the adaptive threshold controller — feedback-driven alarm threshold adjustment based on false alarm rate and recall
  • Develop the CMMS label linking pipeline — match work orders to predictions with confidence scoring
  • Analyze prediction outcomes — precision, recall, F1 by equipment family, by fault type, by site
  • Produce the weekly and monthly accuracy reports

Feature Engineering & Data Quality

  • Define and maintain feature sets for each equipment family — physics-informed features, rolling statistics, cross-tag correlations
  • Monitor data quality metrics — null rates, stale timestamps, schema violations, sensor drift
  • Build the healthy baseline update pipeline — daily computation of per-tag statistics from healthy operating data
  • Implement the training data snapshot pipeline — versioned, reproducible dataset extraction with manifest tracking



Expected Background 

  • 4+ years in machine learning engineering or applied data science
  • Strong Python skills — pandas, scikit-learn, XGBoost/LightGBM, MLflow
  • Experience with time-series data, anomaly detection, or predictive maintenance modeling
  • Understanding of model deployment patterns — model registry, versioning, A/B testing, canary deployments
  • Experience with statistical process control, calibration, or reliability engineering is a plus






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