Berlin based - m/f/d - full-time

What we do at Plato

Plato is the AI-powered Sales Intelligence platform built specifically for B2B wholesale. We transform reactive sales teams into proactive revenue engines by surfacing hidden opportunities in their data. Our platform identifies which customers are at risk, who's ready to buy more, and what products to recommend next - turning thousands of signals into clear next actions.

🏗️ The Wholesale Challenge

Why wholesale is different from everything else you've worked on:

Imagine building ML for a world where:

Your "best customer" might buy twice a year - but spends €500K each time
A customer churning doesn't mean they stopped needing your products - they just found another supplier
Product catalogs contain 100K+ SKUs but even your best customers only know 500 of them
A "trend" might be 3 data points over 18 months
Success means helping a sales rep know which of their 200 customers needs attention today

This isn't e-commerce. This isn't SaaS. This is wholesale.

In B2C, you optimize for the next click. In B2B wholesale, you optimize for the next quarter's relationship. Your models need to understand that a construction company going quiet in winter is seasonal, not churn. That a sudden spike in orders might be a project, not a trend. That recommending the right product category could unlock a 5-year revenue stream.

Your algorithms will power real relationships:

A sales rep uses your churn warning to call a customer - and saves a €2M annual account
Your recommendation engine suggests a product category - opening up €50K in new monthly revenue
Your clustering algorithm reveals that 20 customers have untapped potential worth €5M

Welcome to wholesale AI - where sparse data meets high stakes, where relationships matter more than transactions, and where the right insight at the right time can transform a business.

Short Description

We're looking for a Data Scientist who thrives on solving complex B2B analytics challenges at scale. You'll build ML/AI systems that serve multiple industries simultaneously, tackling problems with sparse data patterns, irregular purchasing behavior, and high business stakes.

Responsibilities

Build and deploy ML models processing millions of transactions across 20+ enterprise customers
Design recommendation engines handling catalogs of 500K+ products
Develop predictive models for customer behavior with sparse, irregular interaction patterns
Create churn prediction and cross-sell systems integrated with ERP systems
Own complete lifecycle from problem definition to production deployment
Build evaluation frameworks connecting model performance to business KPIs
Explanations matter as much as predictions

🚀 What you'll be working on

Production AI Systems at Scale

Build and deploy ML models that process millions of transactions across 20+ (and growing) enterprise customers simultaneously
Design systems that adapt to wildly different data distributions (a construction wholesaler vs. a medical equipment distributor)
Create recommendation engines that handle catalogs of 500K+ products where most customers buy <5% of items
Develop predictive models for customer behavior with sparse, irregular interaction patterns
Design churn prediction and cross-sell systems that integrate seamlessly with ERP (Entereprise Resource Planning) systems
Build models that generate automated proposals and personalized communication templates using LLMs.
Combine classical ML (churn prediction, clustering) with LLMs for insight generation and sales enablement

End-to-End Ownership

Own the complete lifecycle from problem definition to production deployment
Build evaluation frameworks that connect model performance to business KPIs
Design A/B testing infrastructure for continuous improvement
Create feedback loops that learn from real-world outcomes

Complex Technical Challenges

Handle extreme class imbalance (95%+ negative cases) while maintaining business value
Build models that work with limited historical data (cold-start problems)
Design architectures that scale from SMBs with 100 customers to enterprises with 100K customers
Solve multi-objective optimization problems (maximize revenue while ensuring diversity)

🏅 What makes you successful here

Technical Foundation

Strong background in machine learning fundamentals - you understand why algorithms work, not just how to use them
Experience building production data powered products systems that handle real-world messiness
Familiarity in distributed computing (Spark/PySpark) for processing large-scale data
Solid software engineering practices - your code is tested, documented, and maintainable

Problem-Solving Mindset

You approach problems from first principles rather than reaching for standard solutions
Comfortable with ambiguity - you can define success metrics when requirements are vague
You balance technical elegance with business pragmatism
Experience translating business problems into data science solutions

Proven Track Record In:

Building ML systems that handle irregular patterns (time series with gaps, seasonal businesses, etc.)
Working with hierarchical data structures (product taxonomies, customer segments)
Creating models that provide actionable insights, not just predictions
Deploying ML in multi-tenant architectures where one model serves many clients

Bonus Points For:

Experience in B2B analytics, e-commerce, or supply chain optimization
Knowledge of recommendation systems, customer analytics, or revenue optimization
Familiarity with modern data platforms (Databricks, Snowflake, etc.)
Experience with MLOps practices and model lifecycle management
Understanding of European business practices and regulations
Experience with LLM APIs, prompt engineering, or building LLM-augmented products
Proficiency in German

🛠️ Our Tech Stack

You'll be working with modern tools, but we care more about your ability to learn than specific tool experience:

Data Processing: PySpark, SQL, Python
ML Platform: Databricks (Unity Catalog, Workflows, Model Serving)
ML Libraries: scikit-learn, XGBoost/LightGBM, implicit, scipy, OpenAI + experience with your preferred frameworks
Infrastructure: AWS, Terraform
Orchestration: Databricks Workflows, Github Actions
Experimentation: MLflow

🔥 How We Work

High ownership, low process. You own problems end-to-end - from talking to sales to shipping models to production. No handholding, no lengthy specs, no bureaucracy.

Ship fast, iterate faster. The model generating revenue today beats the perfect one still in notebooks. We default to speed - make the call, ship v0, learn, improve.

Direct and intense. Walk over to product. Call the customer. Jump into the code. This is high-intensity work building something hard .

Small team, big impact. We're intentionally small - every person raises the bar. Your code ships to production weekly, your models drive millions in revenue, your insights reach CEOs.

📍 Work Environment

Modern office in Berlin-Mitte with a collaborative team culture
Small, elite team where everyone knows everyone
Direct access to founders and customers - no bureaucracy
Your work ships fast and impacts the business immediately

📝 Interview Process

Initial conversation about your experience and interests
Live coding interview
Technical discussion/case study with the AI team
Meet the broader team and founders

We are excited to hear from you!

Miguel

Compensation: €80K – €130K • Offers Equity

• €80K – €130K • Offers Equity