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Senior Data Scientist - Demand Forecasting (f/m/d)

CompraTica Empleos

EMP:Technology
Hamburg
Tiempo Completo
Remoto
0 vistas

Descripción

We maximize product availability with minimal cashflow investment in 1/10 of the time.

We solve a real problem for SMEs.

The problem we solve: Mid-size Shopify brands lose revenue and cash to stockouts and inefficiencies every day.

They can see the problem.

They can't fix it fast enough.

What VOIDS does: VOIDS is the AI brain for mid-size Shopify brands.

We forecast demand at the product level, catch stockouts and inefficiencies before they happen, and tell e-commerce teams exactly what to do — or execute it automatically with a click.

The result: 98% inventory efficiency.

Six-figure cash unlocked.

Within weeks.

Traction: Launched June 2023.

Since then: 300% growth, 1B+ data points processed, €2M ARR, 50+ brands live — including Hyrox, 6pm, Creamyfabrics, and NatureHeart.

Now targeting €10M ARR by 2027.

Where we're going: Today we own demand forecasting and stock management.

Tomorrow: fully autonomous AI-driven procurement.

We're not building features — we're rebuilding how modern commerce operates.

Why join now: We're a small, fast team where every hire shapes the company's trajectory.

You'll work directly with Jannik and Tobias - two founders who live and breathe e-commerce and AI - and own how we ingest, process, and activate 1B+ data points across our platform.

This isn't a maintenance role.

You'll build the data foundation for a fully AI-driven future.

With high autonomy.

At real data scale.

With real impact.

Tasks 🛠 What you'll do As a Senior Data Scientist – Demand Forecasting, you’ll own the core of our product: the VOIDS demand forecasting engine that currently forecasts €1,000,000,000 of yearly revenue for our customers.

Your mission is to solve our toughest challenge—developing and continuously improving a scalable forecasting solution capable of accurately predicting demand for diverse e-commerce customers.

You'll thrive in complexity, handling varied and dynamic datasets, numerous input variables, shifting market behaviors, and vola.

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