AI Pricing for Subscription Apps

Show the optimal price, product, and paywall to each user

With 3 simple API calls added to your app, we can start building a custom AI model trained on your app's specific performance.

Botsi connects with your existing tech stack, so no migration is needed.

How does it work?
1. Tell Botsi what price points you want to use
2. Send a few data points on app launch
3. Botsi tells you what price to show each user to maximize value

Pretty simple. A few API calls. Custom model trained. Revenue goes up. Works with everything you use today.

Botsi.com to start today.

Botsi

About Botsi:
Botsi is building the AI layer that helps consumer apps show the right offer to the right user. Instead of one-size-fits-all monetization, we use machine learning to present each user with the offer most likely to convert them and grow their lifetime value, in real time, on every decision. We integrate into a company's existing stack and start lifting revenue with minimal engineering lift on their side.
We're an early-stage startup with VC funding, already driving real revenue gains for customers. The core science of what we do is the product: personalization, experimentation, and online decision making. That's the function you'd own.
The role:
This is a senior data science hire to advance our live ML capabilities and build the flexible, scalable architecture that will carry them as we grow. You'll push forward the models that decide which offer each user sees, the experimentation systems that prove they work, and the infrastructure that keeps everything running and learning in production.
We're hiring for ownership. We'll bring you clear goals and objectives; you'll turn them into a roadmap, break it into an action plan, and ship, without needing anyone to hand you the next task. There's also the opportunity to build a team as we grow.
What you'll do:
Advance and ship the ML systems that determine the right offer for each user, trained on each customer's data.Build online decision-making systems, including contextual bandits and reinforcement learning, that learn continuously from conversion feedback.Own experimentation end to end: A/B and sequential testing, causal inference, and uplift and heterogeneous-treatment-effect modeling to prove lift and avoid fooling ourselves.Improve the MLOps stack and collaborate with engineering on data pipelines, feature engineering, training, real-time low-latency serving, monitoring, drift detection, and automated retraining loops.Turn ambiguous business goals ("increase LTV for this segment") into a modeling roadmap, prioritize ruthlessly, and execute.Partner directly with the founders on product and strategy. Your models shape what we sell.
What we're looking for:
A track record of building and deploying ML in production that drove real outcomes (roughly 5+ years, but a strong body of work matters more than a number).Depth in several of: experimentation at scale, causal inference and uplift modeling, contextual bandits or reinforcement learning, Bayesian modeling, recommendation and personalization, or pricing, monetization, and LTV modeling.You've owned the full ML lifecycle, not just notebooks. You can set up end-to-end MLOps flows and keep real-time systems healthy in production.Strong software engineering fundamentals: Python and SQL, cloud (AWS), Docker, and comfort owning production services. Familiarity with probabilistic and ML tooling (e.g. Stan, PyMC, NumPyro/JAX, PyTorch) is a plus.100% self-starter. You're given objectives, not a task list. You build the vision and the plan, and you execute against it. You're comfortable with early stage ambiguity and have a bias to ship and measure.Clear communicator who builds enough trust that we can hand you outcomes and get out of your way.
Bonus Points
Experience with consumer app monetization, growth, or personalization.You've been an early data hire at a startup and know what it takes to build and scale a data function.Real-time, low-latency inference experience.Open-source work, writing, or teaching that shows how you think.
Logistics
Location: Remote within the US, Canada, or LATAM, with meaningful overlap with our working hours for real-time collaboration.Compensation: Equity-forward: meaningful early-employee ownership, plus a competitive salary based on experience.Team: Early stage and VC-backed, fast-moving. You'll have unusual influence over the product and the company.


Senior Data Scientist ML

What the job involves