Data Scientist

Siemens Digital Industries (DI) is an innovation leader in automation and digitalization. Closely, collaborating with partners and customers, we care about the digital transformation in the process and discrete industries. With our Digital Enterprise portfolio, we provide and encourage companies of all sizes with an end-to-end set of products, solutions and services to integrate and digitalize the entire value chain. Meaningful optimization for the specific needs of each industry, our outstanding portfolio supports customers to achieve greater efficiency and flexibility. We are constantly adding innovations to its portfolio to integrate groundbreaking future technologies. We have our global headquarters in Nuremberg, Germany, and have around 75,000 employees internationally.

• Conduct research and develop advanced statistical models and state-of-the-art machine learning algorithms on rapidly evolving structured and unstructured data to support decision-making processes.
• Investigate and assess diverse mathematical, statistical, and AI models to determine the most suitable techniques for solving complex problems.
• Collaborate closely with engineering teams to smoothly transition AI innovations into scalable and dependable operational solutions.
• Effectively communicate findings and insights to technical and non-technical stakeholders through clear and concise reports, visualizations, and presentations, facilitating alignment and driving actionable business decisions.
• Work collaboratively with Software Engineers to ensure seamless integration of data science products into global offerings.
• Maintain a strong product mindset, actively contributing to ideation, design, enhancement, and automation of relevant data products.
• Provide guidance and mentorship to junior data scientists and the wider team on modeling processes and algorithms, fostering a collaborative and innovative work environment.
• Keep abreast of the latest advancements in LLM, NLP, deep learning, and machine learning algorithms, and proactively identify opportunities to leverage new technologies for enhanced solutions.

• PhD or master's degree in computer science, statistics, data science, or a related discipline (e.g., electrical engineering, economics, psychology) with exposure to scientific research and analysis methods.
• +5 years of professional work experience, preferably in a global environment with distributed virtual teams.
• Demonstrate strong data fundamentals and proven ability to organize, clean, structure, and process data, along with a sound understanding of applying data science methodologies to solve complex problems.
• Expertise in fine-tuning and deploying large deep-learning models.
• Experience with generative/multi-modal/multi-task models is advantageous.
• Proficient in Python programming and familiar with challenges associated with working with large datasets.
• Strong academic and publication record is desirable.