Data Scientist Early Hire, Full Model Ownership, B2C SaaS

Role overview

This opportunity is for a data scientist position with a partner employer in Germany, and that partner will handle all applications and the hiring process. The role is designed for someone who wants to make an early impact in a growing data team and take a central role in how machine learning and experimentation shape commercial results.

You will own predictive models end to end, from defining the business problem to deploying, tracking, and improving model performance. The position sits at the intersection of product, growth, engineering, and finance, with a strong focus on turning data into practical decisions that support product direction and revenue expansion. It is a remote-first role with a high degree of independence, broad exposure across the business, and the chance to help establish data science practices from an early stage.

The environment is fast-moving and values experimentation, innovation, and measurable outcomes. It is well suited to someone who enjoys solving complex business challenges with data and wants to see direct influence from their work.

Accountabilities

• Create, test, launch, and oversee machine learning models that support core business goals, including churn prediction, customer lifetime value forecasting, propensity scoring, uplift modeling, and recommendation engines.
• Take responsibility for the full machine learning workflow, covering feature creation, model training, evaluation, deployment, monitoring, and ongoing refinement.
• Keep production models under review so they continue to perform well as customer behavior and product conditions change.
• Plan and evaluate experiments such as A/B tests and causal inference studies to measure business impact and guide decisions based on evidence.
• Help define and strengthen experimentation processes, modeling standards, and team-wide best practices.
• Work with data engineering teams to move models into production using scalable pipelines, feature stores, and reliable deployment methods.
• Convert analytical results and model insights into clear recommendations for product, growth, finance, and leadership teams.
• Spot ways to use AI and machine learning to improve customer experience, product performance, and revenue results.
• Apply privacy-aware modeling methods and follow data governance standards to ensure customer data is handled responsibly.

Requirements

• At least 3 years of hands-on experience building, deploying, and maintaining machine learning models in production.
• Strong command of Python and SQL, plus practical experience with machine learning tools such as scikit-learn, PyTorch, or TensorFlow.
• Good grasp of statistics, experimentation design, hypothesis testing, causal inference, and predictive modeling.
• Proven work on predictive use cases such as churn, lifetime value, propensity, recommendation, or similar applications.
• Ability to own projects from the initial business question through deployment and measurement of impact.
• Strong communication skills and the ability to explain technical findings to non-technical audiences.
• Comfort working independently and making decisions in high-growth, rapidly changing settings.
• Experience partnering with product, engineering, growth, and broader business teams.
• Exposure to MLOps, feature stores, real-time inference, or scalable ML infrastructure is beneficial.
• Experience in B2C SaaS, subscription products, marketplaces, or product-led growth businesses is highly preferred.
• Knowledge of product analytics tools such as Amplitude, Mixpanel, or Segment is an advantage.
• Experience with recommendation systems, NLP, generative AI, or AI-powered product features is a plus.

Benefits

• Remote role with flexibility to work from the Netherlands and other eligible European locations.
• Flexible schedule aligned with European business hours.
• Competitive B2B contract setup.
• 22 paid leave days in addition to public holidays.
• Strong ownership and autonomy, with direct impact on product and business strategy.
• Chance to help shape a growing data function from an early stage.
• Opportunity to work on machine learning initiatives that deliver measurable business value.
• Collaborative, entrepreneurial setting that encourages innovation and continuous learning.
• Close collaboration with senior stakeholders and teams across the organization.
• Opportunity to contribute to AI-driven products and improved experimentation capabilities.

Data privacy and hiring process

The partner employer manages the full application process and all next steps. Applications are reviewed through an AI-assisted matching process designed to shortlist candidates fairly and quickly against the role’s core requirements. The final review and hiring decisions are handled by the employer’s internal team.

By submitting an application, candidates acknowledge that personal data will be processed to assess suitability for the role and may be shared with the hiring employer. This processing is based on legitimate interest and pre-contractual steps under applicable data protection laws, including GDPR. Candidates may exercise rights such as access, correction, deletion, and objection at any time.

AI tools may support parts of the recruitment workflow, such as reviewing applications, analyzing resumes, and checking responses for potential inconsistencies or verification signals. These tools assist recruiters but do not replace human judgment, and final hiring decisions remain human-led.