About DOUBL
DOUBL is a B2B2C fashion technology company. Shopping for clothes online is a guessing game, and it's costing brands and retailers billions in returns and abandoned carts. DOUBL is building the missing data layer in apparel retail. We connect body scan data with garment data to power accurate size and fit recommendations, so shoppers stop guessing and start buying with confidence.
We're a small, fast-moving team building something people genuinely use, and we're just getting started.
The role
No company has been able to solve this problem in the near two decades that it's been attempted. We have a different perspective, and we're looking for someone to lead the way in bringing it to life. We know what we want, but we need someone to guide the how.
This role encompasses the full spectrum from data collection and sourcing to model development, monitoring, and tuning. It also involves roadmap strategy and planning to help us get faster, more user-friendly, and more robust.
What you'll do
- Data Processing & Feature Engineering
- Transform raw, domain-specific data into clean, structured datasets optimized for machine learning algorithms.
- Data Acquisition
- Lead and support strategic data collection, sourcing, and pipeline development initiatives.
- Advanced Model Development
- Design, build, and train advanced predictive models—including neural networks and deep learning—to solve complex business problems, upgrading legacy or heuristic systems with highly scalable ML solutions.
- Model Validation
- Rigorously test and benchmark new models against baseline systems and real-world outcomes to prove performance improvements prior to deployment.
- Technical Communication
- Translate complex model performance metrics and deep data insights into clear, actionable recommendations for non-technical stakeholders.
- Cross-Functional Integration
- Collaborate closely with software engineering teams to seamlessly deploy and integrate ML models into core consumer-facing and enterprise services.
- Strategic Roadmap Planning
- Partner with product management and executive leadership to shape the technical roadmap, defining future data requirements, feature expansions, and opportunities for model innovation.
What we're looking for
- 5 years of experience as a data scientist or machine learning engineer, with real projects taken from data to shipped model.
- Hands-on experience building and training neural networks (PyTorch or TensorFlow)—not just applying pre-built models.
- Strong Python and SQL experience, and comfort building data pipelines from messy, real-world sources.
- Experience collecting, cleaning, and transforming complex data.
- A track record of monitoring models in production and iterating based on live performance, not just offline metrics.
- Comfort working in a small team where you'll influence product decisions, not just execute a spec, and enjoy building something novel rather than adapting an existing solution. This means comfort with high levels of ambiguity and change.
- Experience with GCP.
Nice to have
- Experience modeling complex physical or geometric relationships (e.g., fit, drape, sizing, or similar structured prediction problems).
- Background or interest in apparel, sizing, or retail tech.
- Experience designing experiments (A/B tests) to validate model improvements with real users.
- Experience with personalization or recommendation systems.
Why DOUBL
You'll be the person who takes our fit model from its first generation to its next—building a best-in-the-world product to solve a hard, valuable, and persistent problem, with a clear mandate and real ownership from day one. You'll work directly with the founding team, see your work in the hands of users immediately, and help decide what the product builds toward next. You'll be working alongside a team of top-tier full-stack engineers.
You'll also get coaching and mentorship through the Alberta Machine Intelligence Institute (Amii), one of the world's leading machine learning institutes, to support you as you build this.
How to apply
Send your resume and a link to your portfolio or a data science project you've led, along with an explanation of what you built and why, to Bryn at hello@doubl.ca.
Email Bryn to apply