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A11

Hands-on operations for scaling tech startups

Lead Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeDüsseldorf, GermanyNo salary listed

About the Job

Requirements

  • The role requires 5–8 years of experience in Data Science and Machine Learning, including confident application of statistical methods, regression models, time series analyses, classical machine learning, and modern artificial intelligence algorithms.
  • Candidates must be highly proficient in Python and SQL.
  • Candidates must be able to independently develop value-adding, cloud-based solutions, including generative artificial intelligence use cases such as large language models, retrieval-augmented generation systems, or talk-to-your-data interfaces using artificial intelligence agents or chat interfaces.
  • Candidates must have a proven track record building robust end-to-end machine learning pipelines and maintaining MLOps standards, including continuous integration and continuous delivery, model registry, monitoring, and quality checks.
  • Candidates must have hands-on ownership experience with machine learning models, including feature strategy, training, calibration, and backtesting, and explainability methods such as SHAP or Reason Codes.
  • Candidates must communicate clearly, presenting complex analytical results to business units, IT, and management and translating them into actionable results.

Responsibilities

  • Take full ownership of data-driven analyses and independently derive actionable recommendations to maximize business value.
  • Develop and apply advanced statistical methods, including regression models, time series analyses, and machine learning and artificial intelligence algorithms, and translate results into clear business insights.
  • Design and maintain robust end-to-end machine learning pipelines covering data, features, models, and scoring, including testing, versioning, documentation, and MLOps standards such as continuous integration and continuous delivery, model registry, monitoring, data-quality checks, and incident handling.
  • Collaborate closely with stakeholders and management.
  • Own the SmartRisk Engine within Credit & Risk Management, including feature strategy, training and retraining, calibration, backtesting, drift monitoring, and explainability via SHAP or Reason Codes.
  • Build data-driven use cases such as loss databases, early-warning systems, limit and exposure steering, clustering, and portfolio insights.
  • Develop new cloud-based Data Science solutions, primarily in Python and SQL, to drive measurable business value.
  • Mentor and guide other Data Scientists, encourage innovative thinking, and support the team’s professional growth.

Desired Qualifications

  • Knowledge of credit risk, such as probability of default, exposure at default, loss given default, rating, and International Financial Reporting Standard 9 or expected credit loss.
  • Experience with SAP and Power BI.
  • Experience mentoring and guiding other Data Scientists.
  • Knowledge of German.

About the Company

A11

Hands-on operations for scaling tech startups

A11 embeds its team of experienced operators into fast-growing tech companies to act as an external operations team that drives internal growth. By working alongside founders and business leaders, A11 helps scale various functions from within the client organization, using hands-on, in-house leadership to accelerate expansion. The product works by integrating its operators into the client’s teams, taking on operational roles and driving change across functions such as growth, product, and operations, effectively serving as interim management and growth consulting. What sets A11 apart from competitors is its bench of operators who have already scaled companies to billion-dollar valuations and its ability to embed deeply into a company’s day-to-day operations, rather than offering purely advisory services. Its goal is to help founders navigate rapid growth, optimize scaling processes, and build the operational capabilities needed to achieve large-scale success.

Company size
Not listed
Company stage
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Total funding
Not listed
Headquarters
Not listed
Founded
2025

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