Senior Data Scientist

Company Overview

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Job Summary

We are seeking a Senior Data Scientist to join our DNA Team, specifically focused on the supply chain, procurement, and manufacturing domains. This role is integral to supporting Daimler Truck’s analytics requirements by leveraging advanced data science techniques to optimize business functions.

Responsibilities

• Collaborate with Daimler’s businesses and enabling areas to support key business functions.
• Develop and optimize traditional machine learning models (regression, decision trees, support vector machines, XGBoost, etc.) with a strong emphasis on hypothesis testing.
• Perform feature engineering and dataset preprocessing for effective model training.
• Build scalable machine learning pipelines for real-world applications.
• Engage in time series forecasting, anomaly detection, and recommendation systems development.
• Optimize model performance by employing hyperparameter tuning and cross-validation.
• Deploy machine learning models into production using cloud and on-premises solutions.
• Collaborate with cross-functional teams to integrate machine learning models into business processes.
• Analyze and interpret telematics data to derive actionable insights.
• Develop solutions for streaming data processing and real-time analytics.

Qualifications

• Educational Background: Master’s degree in Statistics, Machine Learning, Economics, or Big Data Analytics (Full-Time).
• Technical Skills:
  • Strong knowledge of Azure components: Azure Data Lake, Azure Data Factory, Azure SQL, Azure Databricks.
  • Proficient in object-oriented programming using Python and PySpark.
  • Experience developing, training, and evaluating machine learning models, including linear regression, logistic regression, k-means clustering, time series forecasting, hypothesis testing (ANOVA, t-test, etc.), random forests, support vector machines, and deep learning algorithms (CNN, ANN, reinforcement learning).
  • Familiarity with statistical distributions, correlation analysis, descriptive statistics, ROC curves, F1-Score, etc.
  • Proficiency in Python, Scikit-learn, and other machine learning libraries.
  • Strong understanding of statistical modeling and data preprocessing.
• Soft Skills: Good communication and presentation skills.
• Practical experience in end-to-end development of data science solutions and deployment, including:
  • Problem definition and solution design.
  • Hypothesis building, testing, and exploratory data analysis.
  • Data engineering for model training, testing, deployment, and MLOps.

Preferred Skills

• Experience with streaming data and telematics data solutions is highly desirable.

Experience

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Environment

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Salary

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Growth Opportunities

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Benefits

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