Head of Applied ML

About the job

We are looking for a Head of Applied ML with 8-10+ years of battle-tested ML expertise to be our Director's technical thought partner and build a world-class ML organization from the ground up.

Our ML team has built impressive capabilities — but we learned on the job. We're smart, we've delivered results, but we know what's missing: someone who's seen ML at scale across multiple organizations.

You'll own ML strategy, mentor the team, prototype new approaches, and help us scale from current ML production to 30+ models daily by 2027. Critically, you'll bridge ML capabilities to product value — ensuring everything we build drives measurable business impact for billion-dollar clients.

This role is 30% hands-on (prototyping, validation) and 70% leadership (strategy, mentorship, team building).

Key Responsibilities

ML Strategy & Roadmap (25%)

- Own ML roadmap: prioritize use cases, define milestones, allocate resources
- Evaluate new ML techniques and determine applicability to industrial IoT domain
- Guide architecture decisions: when to use AutoML vs custom models, when to scale with Ray
- Define ML quality standards: model evaluation, drift detection, retraining policies
- Bridge ML capabilities with CEO's product vision
- Identify gaps in current ML approach and define improvement plans
- Stay current on ML research and translate to industrial IoT applications

Technical Leadership & Mentorship (25%)

- Mentor ML engineers on algorithm selection, feature engineering, model evaluation
- Review complex ML work: validate approaches, suggest improvements, catch issues
- Guide team on problem decomposition: how to tackle ambiguous ML challenges
- Run design reviews for new ML features and use cases
- Establish ML best practices: experimentation tracking, reproducibility, documentation
- Upskill team on advanced techniques: ensemble methods, hyperparameter tuning, interpretability
- Foster culture of rigor and experimentation
- Bring perspectives from previous organizations: "here's how we solved this at X"

Hands-on Applied ML (25%)

- Prototype new ML approaches before team scales them (e.g., should we try neural nets for this use case?)
- Validate algorithm choices for critical use cases
- Jump into thorny technical problems team is stuck on
- Build POCs for new domains or model types
- Debug complex model failures (why is drift happening? why did accuracy drop?)
- Experiment with new techniques and share learnings with team
- Maintain technical credibility through hands-on work

Note:

This isn't daily ML engineering. It's strategic prototyping and validation work that elevates the team.

Team Building & Hiring (15%)

- Scale ML team from 6 to 12+ people over next 18 months
- Define roles and hiring plan with Director (Applied ML Manager, Leads, Engineers)
- Screen ML candidates: assess technical depth and cultural fit
- Conduct technical interviews and make hiring decisions
- Onboard new ML hires: set expectations, ramp up quickly, integrate into team
- Identify skill gaps and upskilling needs
- Build team culture: collaboration, ownership, technical excellence
- Create career paths for ML engineers (junior → mid → senior → lead)

Product Impact & Cross-Functional Leadership (10%)

- Define success KPIs for ML features upfront and track through deployment + long-term runs
- Frame ML problems from business perspective, not just technical lens
- Work with Product team to identify high-impact ML opportunities and gaps
- Collaborate with QA on ML testing methodologies and quality standards
- Partner with Dev Ops on productionization best practices and deployment strategies
- Ensure ML work delivers measurable customer/business value, not just technical excellence
- Translate "what ML can do" into "what product should do"
- Understand system architecture to ensure ML components integrate seamlessly

Note:

This is data-driven product thinking, not product management. You inform product strategy with ML possibilities.

Required Qualifications

- 8-10+ years in ML/Data Science roles (Senior/Staff/Principal level)
- Experience at multiple organizations (bring diverse best practices)
- Track record building production ML systems, not just research
- Led…