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