Manager - ML Delivery & Data Operations

About the job

Mechademy's ML operations are at an inflection point. We've built internal tools that can create production-grade machine learning models in under 30 minutes — even for years of industrial sensor data. The delivery engine exists. Now we need someone to run the factory.

We're looking for a Manager — ML Delivery & Data Operations with 5-7+ years of experience to own our ML model lifecycle, client data onboarding, and operational excellence. You'll work directly with the Director of Data Science to free him from 20-25 hours/week of operational work while scaling ML model production to 30+ models daily by 2027./

Our clients include Berkshire Hathaway, Chevron, SM Energy, and Freeport LNG. When we say "zero-defect execution," we mean it — mistakes aren't an option when you're monitoring billion-dollar industrial assets.

This role is 50% operations management and 50% hands-on execution initially, shifting to 70% management as the team scales.

Key Responsibilities

Operations Management & Process Excellence (30%)

- Triage incoming requests (model creation, data onboarding, ad-hoc analyses) and distribute work across the team
- Establish SLAs for ML operations and data operations — define what "good" looks like and hold the team to it
- Build processes, SOPs, and automation to reduce the current 80% manual operational burden by 40%+
- Capacity planning: scale from current pace to 30+ models daily by 2027
- Identify operational bottlenecks and implement systematic solutions
- Free the Director from operational firefighting, enabling 90% strategic focus

ML Model Lifecycle Management (25%)

- Use our internal AutoML tools to create regression models for clients (training takes
- Validate model quality — you need to understand feature engineering, feature selection, evaluation metrics (not just accuracy — residuals, drift, business-relevant metrics), and know whether a model is good enough to ship to Chevron
- Deploy models to production environments and monitor for drift and degradation
- Manage model retraining schedules and lifecycle
- Build automation for model monitoring (currently manual scripts)
- Transition from 80% manual ML ops to automated, scalable processes

Client Data Onboarding & Quality Assurance (25%)

- Lead client dataset onboarding from raw IoT sensor data to ML-ready state
- Prepare data for ML model training using our AutoML platform
- Write and optimize SQL queries to inspect, transform, and validate client data
- Implement rigorous DQA workflows: type checks, missingness detection, outlier flagging, reconciliation
- Partner with Customer Success, Product, and Engineering to resolve data blockers
- Ensure zero defects in client data entering ML pipelines

Team Leadership & Hiring (20%)

- Directly manage 2-3 people initially, grow team to 6-7 over 12-18 months
- Conduct weekly 1:1s, performance reviews, career development planning
- Hire and onboard ML/Data Ops Specialists with Director approval
- Create SOPs, training materials, and knowledge transfer processes
- Foster culture of rigor, craftsmanship, and zero-defect execution

Required Qualifications

- 5-7+ years in data operations, analytics delivery, ML operations, analytics engineering, or similar operational roles
- 2+ years with direct team management responsibility (not just tech lead)
- Strong proficiency in Python (Pandas, Num Py, Polars); production-quality code, not just notebooks
- Write optimized SQL queries for large datasets; query tuning, window functions, CTEs
- Solid understanding of ML concepts — you should know what feature engineering and feature selection are, why models are created, how to evaluate whether a model is performing well, and what deployment means in practice. You don't need to design algorithms, but you need to look at a model's output and know whether it's good enough to ship.
- Data validation, cleaning, anomaly detection, automated DQ workflows
- Scripting for process automation, scheduling, orchestration
- Demonstrated track record of building processes from scratch: SOPs, automation, SLAs, capacity planning
- Process-driven mindset: you see a manual process and instinctively ask "how do I automate this?"
- Comfortable…