Data Scientist

🕒 March 4

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Codvo.ai

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2019

🔒 Cybersecurity

☁️ SaaS

AI • Cybersecurity • SaaS

Codvo. ai is a technology company that specializes in delivering strategic enterprise solutions through advanced AI-driven innovation. They focus on transforming enterprise data into measurable value by helping businesses accelerate growth with custom AI implementations tailored to meet the specific challenges of various industries. Their extensive service offerings include AI/ML automation, application development, data analytics, cybersecurity, and digital transformation, ensuring that organizations can thrive in a rapidly evolving digital landscape.

📋 Description

• Model development, training pipeline, and analytics backend • 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 • 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 • 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

🎯 Requirements

• 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

🏖️ Benefits

• Health insurance • Career development opportunities