Ad Operations Manager

Jobtailor; Deutschland; Vor Ort; EUR 65.000 - 95.000; Vollzeit

Zusammenfassung

Jobtailor in Germany seeks an experienced data scientist to design predictive models and extract actionable insights from large datasets. You will collaborate with stakeholders to translate business questions into analytics tasks and deliver dashboards that communicate results effectively.

The role emphasizes feature engineering, model validation, and building scalable data pipelines in an Agile setting. Strong Python, R, and SQL skills are essential.

Qualifikationen

• 5+ years of experience in data analysis, statistics, or data science.
• Strong background in statistical modeling and ML techniques (regression, clustering, PCA, random forest).
• Proficiency in Python, R, and SQL for analysis and modeling.
• Experience with SAS, RStudio, or Jupyter Notebooks.

Aufgaben

• Design and implement predictive models and statistical analyses to support business initiatives.
• Apply supervised and unsupervised ML techniques for customer segmentation, churn prediction, and behavioral analysis.
• Partner with business stakeholders to frame analytical problems and translate them into actionable insights.
• Prepare, clean, and transform large datasets to support modeling and analytics.
• Develop and maintain reproducible analytical workflows using Python, R, SQL, or similar tools.
• Build dashboards and reports to communicate results and support decision-making.
• Evaluate model performance and improve through experimentation and validation.
• Promote a data-driven culture by educating stakeholders on analytics methodologies.
• Collaborate in cross-functional teams in Agile environments.

Kenntnisse

Python

R

SQL

Statistics

Machine learning

Feature engineering

Data pipelines

Data visualization

Tools

SAS

RStudio

Jupyter Notebooks

Jobbeschreibung

Responsibilities

• Design and implement predictive models and statistical analyses to support business initiatives.
• Apply supervised and unsupervised machine learning techniques to solve problems such as customer segmentation, churn prediction, and behavioral analysis.
• Partner with business stakeholders to frame analytical problems and translate them into actionable insights.
• Prepare, clean, and transform large datasets to support modeling and advanced analytics.
• Develop and maintain reproducible analytical workflows using Python, R, SQL, or similar tools.
• Build dashboards and analytical reports that help communicate results and support decision-making.
• Evaluate model performance and continuously improve models through experimentation and statistical validation.
• Promote a data-driven culture across the organization by educating stakeholders on analytical methodologies.
• Collaborate in cross-functional teams working in agile environments.

Requirements

• 5+ years of experience working with data analysis, statistics, or data science.
• Strong background in statistical modeling and machine learning techniques (e.g., regression, clustering, PCA, random forest).
• Experience working with large datasets and customer analytics.
• Proficiency in Python, R, and SQL for data analysis and modeling.
• Experience using statistical tools such as SAS, RStudio, or Jupyter Notebooks.
• Strong knowledge of data preparation, feature engineering, and model validation techniques.
• Ability to translate business problems into analytical solutions.
• Experience working in data-driven organizations and communicating insights to business stakeholders.
• Strong analytical thinking and problem-solving skills.
• Familiarity with customer lifecycle analysis, churn modeling, or marketing analytics (Nice to Have).
• Experience building data pipelines or centralized data models (Nice to Have).
• Knowledge of A/B testing and statistical significance testing (Nice to Have).
• Experience working in Agile/Scrum environments (Nice to Have).
• Exposure to big data environments or cloud-based analytics platforms (Nice to Have).

Core Competencies

Demonstrates expertise in predictive modeling, statistical analysis, and machine learning techniques to derive actionable insights from large datasets. Proficient in Python, R, and SQL, with a strong focus on data preparation, feature engineering, and model validation.