Data Scientist

Role overview

This opportunity is being shared through a partner company that will handle applications and the next stages of the process. The employer is seeking a Data Scientist located in Germany to join a fast-moving, product-led team where analytics and machine learning have a direct influence on user experience and company results.

The position focuses on building and deploying machine learning solutions for personalization, search, and recommendation capabilities used at scale by a global audience. You will combine statistical modeling, Python development, and cloud-based tooling to solve practical business challenges. The role is highly collaborative and remote-first, with an emphasis on measurable outcomes and ownership of end-to-end ML systems.

Key responsibilities

• Build, refine, and put into production machine learning models that strengthen personalization, search relevance, and recommendation performance.
• Create data-driven algorithms that improve user experience and contribute to higher engagement and conversion.
• Use advanced statistical techniques and data-mining methods to uncover insights from large datasets.
• Partner with engineering and product stakeholders to embed ML models into live systems while maintaining reliability and scale.
• Track model performance over time and adjust approaches using production data and business feedback.
• Convert business challenges into analytical and machine learning solutions that support key metrics.
• Work with cloud infrastructure and data pipelines to enable scalable machine learning workflows.
• Support experimentation design and A/B testing to measure the impact of model changes.

Requirements

• At least 2 years of experience in a Data Scientist, Quantitative Analyst, Quantitative Researcher, or similar analytical role.
• Strong hands-on experience with Python and common data science libraries such as pandas, NumPy, and scikit-learn.
• Sound understanding of probability, statistics, machine learning, and linear algebra.
• Demonstrated success applying ML models to practical business problems with measurable revenue or KPI impact.
• Working knowledge of SQL and relational databases.
• Experience with cloud environments such as AWS, GCP, or Microsoft Azure is preferred.
• Exposure to production ML deployment and full data science pipelines is important.
• Comfort working in a fast-paced, product-focused setting with cross-functional teams.
• Strong communication skills with the ability to explain technical ideas to non-technical audiences.
• Bachelor’s or Master’s degree in Applied Mathematics, Computer Science, Engineering, Financial Engineering, or another quantitative discipline.
• Using AI tools or agents in the development workflow is an added advantage.
• Knowledge of Apache Spark and/or prior experience in the gaming sector is a plus.

Benefits and additional information

• Fully remote-first setup with flexible working arrangements.
• Competitive pay plus quarterly performance-based bonuses.
• 28 paid vacation days each year.
• Flexible working hours, with core availability from 10:00 am to 3:00 pm local time.
• High-end equipment is provided to support your work.
• Annual company retreats for team connection and collaboration.
• Referral and performance-based bonus programs.
• The chance to work on large-scale machine learning systems that have direct business impact.
• Strong focus on professional growth and exposure to modern ML and cloud technologies.

Data privacy and hiring process

Applications are reviewed through an AI-supported matching process designed to assess fit quickly, consistently, and fairly against the role’s core criteria. The strongest matches are shared with the hiring company, and the employer’s internal team manages interviews, assessments, and final decisions.

By submitting an application, candidates agree that personal data may be processed to evaluate candidacy and share relevant details with the hiring employer in line with applicable data protection laws, including GDPR. The basis for this processing includes legitimate interest and pre-contractual steps. Applicants may exercise rights such as access, correction, deletion, and objection at any time.

AI tools may also be used to assist with parts of recruitment, including reviewing applications, analyzing resumes, and checking responses for possible inconsistencies or verification signals. These tools support the recruitment team but do not replace human judgment, and final hiring decisions are made by people.