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 DevOps 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 ML strategy or roadmap in previous roles
Experience shipping ML features that drove measurable product/business impact
Expert in ensemble methods, feature engineering, model evaluation, hyperparameter tuning
Strong understanding of ML operations: deployment, monitoring, retraining, drift detection
Define ML success metrics and translate to business value
Strong communicator: explain complex ML concepts to engineers, product managers, and executives
Hands-on coder who stays technical despite leadership responsibilities
Comfortable with ambiguity: can define strategy when roadmap isn't fully clear
Cross-functional collaboration: worked effectively with Product, QA, DevOps teams
Hiring experience: assessed ML candidates and made hiring decisions
Collaborative mindset: work with Director as thought partner, not compete
Product-minded: balance technical excellence with business/user value

Preferred Qualifications

Experience with neural nets, deep learning, reinforcement learning
Industrial IoT, sensor data, or time-series data experience
Experience with Ray, Kubeflow, MLflow, or similar platforms
Published research or patents
Master's or PhD in CS, ML, Statistics, or related field (but experience > credentials)
Comfortable with time-series, sensor data, or industrial domains (or eager to learn)
Domain transfer experience: applying ML across multiple industries

Technologies You'll Work With

Languages: Python (scikit-learn, XGBoost, pandas, numpy), SQL
ML Operations: MLflow, Ray, model deployment, monitoring, drift detection
ML Techniques: Regression, classification, time-series, anomaly detection, ensemble methods
MLOps: Model lifecycle management, retraining pipelines, automated monitoring
Cloud Platforms: AWS/GCP/Azure (either is fine)
Automation: AutoML tools, distributed computing (Ray, Spark)
Collaboration: Product, QA, DevOps cross-functional work

Qualifications

Bachelor's degree (B.Tech/B.E.) in Computer Science, Machine Learning, Statistics, or related engineering discipline
Master's or PhD in CS, ML, Statistics preferred (but experience > credentials)
Solid foundation in algorithms, data structures, and ML theory

Bonus Points

Led ML teams from 5 to 15+ people in high-growth startups
Experience scaling ML production from low to high volume (10x+ model creation)
Built ML strategy and roadmap from scratch at previous companies
Mentored junior/mid ML engineers who became senior/staff level
Shipped ML features that drove 10x+ business/product impact
Experience with industrial/manufacturing domains or sensor/time-series data
Track record prototyping 5+ new ML techniques and productionizing 2+
Successfully hired and onboarded 5+ ML engineers

Apply now

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