• Design and Build Data Pipelines: Construct and maintain robust, scalable data pipelines to seamlessly integrate data from multiple sources • Data Acquisition and Storage: Develop efficient database solutions to collect, store, and secure data, ensuring accessibility and protection • Data Transformation: Create algorithms to convert raw data into actionable insights, aligning with our business objectives • Pipeline Optimization: Continuously evaluate and enhance existing data systems and processes for improved efficiency and reliability • Quality Assurance: Implement systems to validate and ensure the accuracy and consistency of data sets • Collaboration: Work closely with machine learning engineers, data scientists, analysts, and other stakeholders to meet the organization's data-centric needs

• You develop the end-to-end automation of data pipelines to make ingestion, transformation, and distribution of data more efficient and self-service for our users • You explore and learn the latest AWS and big data technologies to uncover hidden use-cases, enable new capabilities, and build new integrations for our SIXT Data shop product • You interact with Data Engineers, BI Analysts and Data Scientists to find the best solutions for different analytical use cases (Dashboarding, ad hoc Analytics, Data-as-a-product, Machine Learning) • You contribute to the Data Platform vision and execution with experience, new ideas, and your own curiosity • You show extreme work ethics and integrity to deal with sensitive data while protecting our end-customer

Role Description As a Data Science Intern, you will collaborate with our data science and analytics teams to work on meaningful projects involving data analysis, predictive modeling, and statistical modeling. You will have the opportunity to apply your academic knowledge in a practical, fast-paced environment, contribute to key data-driven projects, and gain valuable experience with industry-leading tools and technologies. - Assist in collecting, cleaning, and preprocessing data from various sources. - Perform exploratory data analysis to identify trends, patterns, and anomalies. - Develop and implement machine learning models and algorithms. - Create data visualizations and reports to communicate findings to stakeholders. - Collaborate with team members on data-driven projects and research. - Participate in meetings and contribute to discussions on project progress and strategy. - Work with large datasets to clean, preprocess, and analyze data. - Build and deploy statistical and machine learning models to generate actionable insights. - Conduct exploratory data analysis (EDA) to uncover trends, patterns, and correlations. - Assist in the creation of data visualizations and dashboards for reporting insights. - Support the development and improvement of data pipelines and algorithms. - Collaborate with cross-functional teams to understand data needs and translate them into actionable analytics solutions. - Contribute to the documentation and presentation of results, findings, and recommendations. - Participate in team meetings, brainstorming sessions, and project discussions. Qualifications - Any Graduate / PassOuts / Freshers can apply. - Currently pursuing a Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field. - Proficiency in programming languages such as Python, R, or SQL. - Strong foundation in statistics, probability, and data analysis techniques. Requirements - Duration: 2 Months (with the possibility of extending up to 6 months) - MODE: Work From Home (Online) Benefits - Internship Certificate - Letter of recommendation - Stipend Performance Based - Part time work from home (2-3 Hrs per day) - 5 days a week, Fully Flexible Shift

• Design, implement and operate solutions for hardware design data migration within complex semiconductor hardware design environments • Set up, configure and validate temporary mitigation or transition systems for hardware design data (e.g., EDA data management or configuration management systems) • Develop data migration concepts and strategies for transitions between different hardware design data management, configuration management and lifecycle systems (e.g., from legacy platforms to modern PLM/IPLM or version control solutions) • Plan, execute and validate export and import procedures using sample, test and production data • Define and document data topologies, vault/repository structures, data paths, mappings and inventories • Develop, review and validate migration and automation scripts, ensuring data integrity, consistency and completeness • Support server-side and hardware design infrastructure configurations, toolchain and EDA integrations (e.g., Cadence-based environments / DFII) • Collaborate closely with engineering, IT and infrastructure teams and contribute to testing, reviews, documentation and end-user readiness