Bosch is looking for a Data Science Intern to work on innovative projects and gain hands-on experience in data science focused on automotive applications, solving real-world automotive powertrain problems using advanced analytics and machine learning techniques.
Requirements
- 1+ year academic or industry experience with software languages and operating systems.
- Data Exploration and Analysis: Collect, clean, and analyze large and complex datasets to identify patterns, trends, and insights. Apply statistical methods and data visualization techniques to communicate findings effectively.
- Machine Learning Development: Develop and implement machine learning models, algorithms, and statistical techniques to solve specific business challenges. This may involve tasks such as data preprocessing, feature engineering, model training, and evaluation.
- Predictive Modeling and Optimization: Build predictive models to forecast future outcomes and optimize business processes. Apply regression, classification, clustering, and other machine learning techniques to solve problems related to demand forecasting, anomaly detection, quality improvement, etc.
- Data Mining and Pattern Recognition: Discover meaningful patterns, correlations, and relationships in large datasets. Use techniques like association rules, clustering, and dimensionality reduction to uncover insights and generate actionable recommendations.
- Automotive-related hobby and/or experience working with engines or vehicles.
Responsibilities
- Data Exploration and Analysis: Collect, clean, and analyze large and complex datasets to identify patterns, trends, and insights. Apply statistical methods and data visualization techniques to communicate findings effectively.
- Machine Learning Development: Develop and implement machine learning models, algorithms, and statistical techniques to solve specific business challenges. This may involve tasks such as data preprocessing, feature engineering, model training, and evaluation.
- Predictive Modeling and Optimization: Build predictive models to forecast future outcomes and optimize business processes. Apply regression, classification, clustering, and other machine learning techniques to solve problems related to demand forecasting, anomaly detection, quality improvement, etc.
- Data Mining and Pattern Recognition: Discover meaningful patterns, correlations, and relationships in large datasets. Use techniques like association rules, clustering, and dimensionality reduction to uncover insights and generate actionable recommendations.
- Collaborative Problem-Solving: Work closely with cross-functional teams, including business stakeholders and software developers, to understand requirements and develop data-driven solutions. Collaborate effectively, share knowledge, and contribute to the overall success of the projects.
- Documentation and Reporting: Document methodologies, algorithms, and results in a clear and concise manner. Prepare reports and presentations to communicate findings and recommendations to both technical and non-technical audiences.
- Continuous Learning: Stay up-to-date with the latest advancements in data science, machine learning, and related technologies. Actively participate in training programs, workshops, and knowledge-sharing activities to enhance your skills and expertise.
Other
- Must be currently enrolled in an accredited university and pursuing a Bachelors or Masters degree in Engineering, with at least one semester completed
- A minimum 3.0 GPA
- Must be a minimum of 18 years of age
- Must work 30-40 hours per week
- Indefinite U.S. work authorized individuals only. Future sponsorship for work authorization unavailable.
