At Solve, we're revolutionizing how online brands connect with their customers through messaging. We're not just sending emails, we're building the future of intelligent customer engagement using AI. Our platform helps fashion, cosmetics, and lifestyle brands send the right message to the right customer at exactly the right time – eliminating the endless hours of manual segmentation, A/B testing, and campaign optimization that plague traditional marketing.

Backed by leading venture capital firms Bowery, Airtree, and Movac, our team spans New Zealand and serves customers throughout North America. After successful beta testing with several brands, we're preparing to launch our next-generation product that lets marketers focus on creativity while our AI handles the heavy lifting.

We’re looking for a Data Scientist with experience designing and implementing models in environments with sparse data, no clear validation set, and a need for adaptive, probabilistic decision-making. Our ideal team-mate has a strong foundation in Bayesian methods or related approaches (e.g., reinforcement learning), combined with hands-on experience building and deploying these systems in production. You should be comfortable working without the structure of traditional supervised learning and have a track record of delivering real-world impact through intelligent, uncertainty-aware modeling.

What you'll do:

Work closely with our Product team to turn ideas into reality, ensuring we're building exactly what our customers need
Translate complex statistical and ML concepts into scalable solutions that integrate with our application
Design, develop, and maintain robust, scalable data models to process and transform large datasets for machine learning and analytics applications.
Collaborate with our engineering team to deploy, monitor, and maintain production ML systems
Think strategically about how technical decisions impact our ability to serve customers effectively and help identify the best practices and tooling to achieve successful outcomes.

Essential Skills

Probabilistic Modeling Under Data Sparsity
You have built models that work with limited, noisy, or sparse data
You understand how to reason under uncertainty and make use of prior knowledge
You can design and implement Bayesian models or similar frameworks (e.g., reinforcement learning with belief states)

No-Rules Modeling Without a Validation Set

You have experience building systems where there’s no clean way to validate predictions
You are able to monitor model behavior and performance via practical heuristics and metrics

Theoretical + Practical Depth

You have a strong grasp of Bayesian reasoning, RL, or dynamic updating frameworks
You have deployed models into production, not just built them in notebooks
Your work has led to measurable business or product impact

Preferred Skills

Efficient System Design & Computational Thinking
You can build scalable, efficient models that work across large user bases or real-time systems
You understand memory, speed, and parallelization trade-offs in model execution

ML System Architecture Knowledge

You are familiar with ML infrastructure and deployment patterns (e.g., feature stores, batch/stream processing, model serving)
You can contribute insights to existing engineering teams on system design, data handling, and automation

Marketing/Engagement Domain Familiarity

You have experience in email, ads, personalization, or lifecycle optimization is helpful, but not essential
You understand click behavior, conversion modeling, or user segmentation

Our Tech Stack