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.
What you'll do
- Develop the forecasting engine that fuels VOIDS demand forecasting services, directly influencing customer outcomes and satisfaction
- Design and implement scalable forecasting methodologies adaptable to a diverse customer base and unique datasets
- Actively engage with customers, gathering deep insights and feedback to ensure our forecasting solutions meet their evolving needs
- Collaborate closely with the CTO, CEO, customer success, and engineers
- Identify and execute strategic improvements in scalability, accuracy, and performance of forecasting systems
- Enhance developer experience, advocating best practices, and upgrading tooling within the data science and engineering teams
- Run our forecasting operations, making sure fresh and stable models and forecasts are shipped to our customers reliably
- Actually get things done, deciding yourself what to focus on — without bureaucracy
Requirements
- Fluent English communication skills; German is a plus
- Clear, professional, and asynchronous communication abilities
- 3+ years of Data Science experience, including at least 2 years specifically in time series forecasting (preferably consumer products)
- Experience building and maintaining pipelines and APIs for model training/inference, using tools such as Airflow/Dagster, AWS Sagemaker, MLflow, etc.
- Hands‑on experience with SQL databases, ideally PostgreSQL
- Delegating work to entire AI workflows and shipping AI‑enabled data / modelling pipelines where actual decisions and work is done by AI. We want to build a tech stack that can e.g. pick the right setup for a customer with the help of AI
- Strong product and customer intuition and a proactive, ownership‑oriented mindset
- Comfort with ambiguity and autonomy in problem‑solving
Bonus / Nice‑to‑Have
- Experience with eCommerce and/or B2B SaaS startups
- Background in data engineering for scalable data pipelines, to cover the whole data pipeline more full‑stack
- Familiarity with infrastructure frameworks (Terraform, Kubernetes, etc.)
- Exposure to technologies for handling larger data sets such as BigQuery, Spark etc.
- Contributions to developer experience and internal tooling improvements
- Practical experience with forecasting tools such as Nixtla, Darts, statsmodels, sktime, etc.
Tech Stack
- Programming: Python (Pandas, Polars), SQL
- Modeling: Statistical, ML, and neural time series models (mostly Nixtla)
- Data Storage: PostgreSQL, AWS S3 (Parquet)
- ML Infrastructure: AWS SageMaker, AWS Lambda, MLflow
- Orchestration: Airflow on AWS
- Collaboration & AI Tools: GitHub Copilot, ChatGPT
Benefits
- Permanent full‑time contract (no B2B)
- Competitive salary (€80,000–€100,000) + Equity
- 30 days paid vacation
- All AI subscriptions with unlimited usage you want
- New Mac Book Pro & min. 2 Monitors in the office
- Regular team events and quarterly off‑sites
- Real ownership and influence
- A calm, focused work environment that rewards initiative
- Wellpass membership to unlimited fitness, yoga, swimming, climbing, and more
We care less about titles and more about impact, so we look forward to talk to you and learn more about:
- A forecasting model you built and what complexity you dealt with
- How you currently use AI in your daily engineering workflow — concretely, not in theory
- What motivates you, and what kinds of data problems you find genuinely interesting
