Data Scientist (Dallas)

Codvo.ai; Dallas (TX); On-site; USD 100,000 - 120,000; Full time

Job summary

A leading technology company located in Dallas, Texas is seeking a Data Scientist with over 5 years of experience in applied data science or machine learning. The ideal candidate should be familiar with predictive maintenance and anomaly detection and possess strong time-series analysis skills. You will monitor alerts, facilitate sessions with maintenance teams, and document tuning decisions. An understanding of HVAC, mechanical, or electrical systems is highly advantageous. A collaborative and innovative work culture awaits you.

Qualifications

• 5+ years in applied data science or machine learning.
• Strong understanding of time-series analysis and classification models.
• Ability to explain model behavior to non-technical maintenance engineers.

Responsibilities

• Conduct the BMS protocol audit with the customer's controls engineer.
• Monitor live anomaly scores and triage alerts.
• Facilitate alert review sessions with site maintenance leads.
• Travel to Wave 1 sites for the discovery phase.
• Train offshore Data Scientist on site-specific tuning.

Skills

Predictive maintenance

Anomaly detection

Time-series analysis

Classification models (XGBoost, LightGBM)

Model behavior explanation

Job description

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

The on-site technical presence during the pilot and Wave 1. Works directly with the customer's maintenance team to validate alerts, tune models, and build trust in the platform's outputs. This role requires someone who can explain machine learning predictions in maintenance engineering language, not data science jargon.

Responsibilities

Discovery & Baseline (Weeks 1–2)

• Conduct the BMS protocol audit alongside the customer's controls engineer — identify available tags, data quality, polling rates, historian configuration
• Extract and analyze the 30-day historian pull — identify operating modes, load patterns, seasonal variations, and data gaps
• Build the baseline operating profile for each monitored equipment unit
• Review the customer's maintenance logs — identify the 3–5 known fault events that will serve as ground truth validation
• Map the customer's equipment taxonomy to NEIO's equipment family catalog — confirm coverage, flag gaps

Model Tuning & Validation (Weeks 3–5)

• Monitor live anomaly scores and triage the first alerts — determine true positive vs. false positive vs. ambiguous
• Facilitate the alert review sessions with the site maintenance lead — present alerts in context, gather feedback, document site-specific operating knowledge that affects interpretation
• Tune the physics constraint gate to the site's operating ranges — adjust thermodynamic bounds, load-dependent thresholds, equipment-specific parameters
• Calibrate fault classification confidence thresholds — Platt scaling, ECE measurement, reliability diagram review
• Validate time-to-failure estimates against known maintenance history
• Document all tuning decisions and rationale — this becomes the playbook for fleet rollout

Phase 2 (Wave 1 Sites)

• Travel to Wave 1 sites for the 1-week discovery phase at each site
• Conduct on-site tag mapping review with each site's controls engineer
• Facilitate the alert review session at each Wave 1 site
• Train the offshore Data Scientist on site-specific tuning decisions so they can handle
• Wave 2 remotely

Expected Background

• 5+ years in applied data science or machine learning — predictive maintenance, anomaly detection, or industrial process optimization
• Strong understanding of time-series analysis, classification models (XGBoost, LightGBM), and calibration techniques
• Ability to explain model behavior to non-technical maintenance engineers — "the model flagged this because superheat is trending 3 degrees above normal for this load condition" not "the feature importance vector shows..."
• Comfortable working on-site at a data center or industrial facility
• Experience with HVAC, mechanical, or electrical systems is a strong advantage — someone who knows what a chiller does, what COP means, why approach temperature matters