What are my job responsibilities?
This position will be responsible for taking on projects with clients, formulate KPIs and develop consistent quality work to help drive customer’s decisions.
The ideal candidate must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. The successful Data Scientist will work alongside the existing Lead Data Scientist in the research, selection, customization and application of Machine Learning and Deep Learning models, applied to disparate IIOT data sources.
- Accessing and analyzing data to generate insights and make proactive recommendations.
- Formulating success metrics, KPIs and creating dashboards/reports to monitor them.
- Designing and analyzing experiments to test new ideas for improving the customer business.
- Developing models and data-driven solutions that add value to the mobility market.
- Work in a collaborative team environment with other highly skilled specialists in statistics, machine learning, and software engineering.
- Implement analytical models into production by collaborating with software developers and machine learning engineers
- Develop customer-oriented solutions to diagnose Rolling Stock and Infrastructure assets and to perform preventive maintenance actions
- Promote a Data-driven customer centric culture at Siemens.
- All listed tasks and responsibilities are deemed as essential functions to this position; however, business conditions may require reasonable accommodations for additional task and responsibilities.
What do I need to qualify for this role?
- Minimum 2 years of experience in a relevant role.
- Strong problem-solving skills with an emphasis on analytics solutions development.
- Interest and passion for big data technologies (e.g., Hadoop, Mahout, Pig, Hive);
- Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.
- Deep knowledge in data science methods (e.g. supervised and unsupervised learning, time series analysis in time and frequency domain, deep neural networks, time series prediction, Bayesian networks and deep learning in the field of predictive maintenance …).
- Exposure/ experience to tools & Infrastructure such as Jenkins, GITHUB, Eclipse is preferred.
- Experience working in an Agile development environment
- Experience in Predictive Maintenance
- Promote communication and collaboration
- Transfer knowledge to younger data scientists.
- Degree or above in a quantitative discipline: Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering, Economics, etc.