ML/Data Science Engineer

Our client is an early stage US-based startup who have developed an AI-powered 'monetization kit' that helps businesses with pricing packaging and billing.

They are seeking to hire a backend-focused ML/DS Engineer who will be *based in Canada* and work remotely.

This is a full-time, permanent, remote position for candidates with Canadian work authorization.

Role Overview

We're seeking a data scientist/machine learning engineer to build the intelligence behind our client's next-generation revenue optimization platform. You'll design and implement statistical models, pricing algorithms, and recommendation engines that help businesses optimize their monetization strategies through AI-driven insights. This role combines deep analytical expertise with practical engineering skills, requiring someone who can architect ML solutions while contributing to their backend infrastructure when needed.

What You'll Build

Revenue optimization algorithms using customer usage behavior and purchasing patterns to suggest pricing improvements
Statistical models for dynamic pricing recommendations and business model optimization
Machine learning pipelines that analyze competitive landscapes and recommend pricing strategies
Recommendation engines that suggest optimal plan structures and feature entitlements
Analytics systems that process large datasets to surface actionable revenue insights
Backend integrations to connect ML models with our billing and monetization infrastructure

Required Experience & Skills

5-7+ years of data science/machine learning experience with production systems
Strong statistical analysis background - comfortable with algorithms like K-means clustering, random forests, decision trees, and Bayesian analysis
Python expertise for data science workflows and model development
SQL proficiency for data manipulation and analysis
Product analytics experience - understanding user behavior data and conversion optimization
Some backend engineering capability - able to contribute to our TypeScript/Node.js stack when needed
AWS familiarity - their entire infrastructure runs on AWS
Startup experience with rapid iteration and working under ambiguity

Technical Stack & Environment

Primary: Python for ML/data science work, SQL for data analysis
Backend: TypeScript, Node.js, PostgreSQL, Prisma ORM (AWS-hosted)
Approach: Vanilla, tried-and-true technologies over cutting-edge experiments
Scale considerations: Building for anticipated growth with security-first mindset (handling financial data)

Ideal Background & Mindset

Data science depth over engineering breadth - we need someone who can help to upskill the team in ML optimization and testing
Product-minded approach - understanding how technical decisions impact business outcomes
Startup mentality - comfortable wearing multiple hats and context-switching between analysis and implementation
Risk-aware perspective - someone who can guide founders away from technical landmines based on experience
Business context awareness - empathy for how pricing decisions affect customer behavior and revenue

What We're Looking For

This role is 70% data science/ML work, 30% backend engineering. You'll spend most of your time analyzing usage patterns, building recommendation algorithms, and optimizing pricing models. When needed, you'll contribute to our TypeScript backend to integrate your models into our production systems.

We need someone who can balance mathematical rigor with practical implementation - someone who can determine whether our current recommendation algorithm is optimized and knows how to test and improve it using proper statistical methods.

Company Culture Fit

Startup for adults - professional, respectful environment with high expectations for self-management
Ruthless prioritization - we debate intensely to find the right approach, then execute without distraction
Family-conscious - founders who respect work-life balance while demanding focused effort
Flat organization - best idea wins regardless of seniority level
International & flexible - remote-first with quarterly in-person intensive work sessions
Direct communication - transparent, ego-free environment focused on results