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