We are looking for a Data Scientist in our Digital Services Team who leads the end -to-end cycle of developing, testing and maintaining architectures, organizing big data and ETL pipelines and develops advanced analytical models based on appropriate AI (ML / DL / NLP / Computer vision etc.) frameworks
• Design and maintain data architectures to improve data reliability, quality and efficiency
• Research and deploy data acquisition techniques from various systems to optimize business and solution requirements
• Research and build analytical models and experiment with multi-dimensional hypothesis to ensure scalability and actionable insights
• Design & develop ETL pipelines for data-driven software applications
• Design and execute experiments to test the feasibility of analytics solutions / tech stack options, to solve business problem hypotheses
• Build reusable pipeline and model components around the application of advanced analytics (ML / AI / Deep-Learning etc.), to smart factory / Industry 4.0 domain
• Work closely with multiple stakeholders such as the IoT Lead, IoT Architect (Software), IoT Architect (Automation) to constructively build solutions for customers
• Develop data strategy which focus on outcomes and integrations which focus beyond functional requirements
• Benchmark and research industry and business standards, including research publications, social media and the open-source community, to develop models and patterns that can be predictive and prescriptive modeling.
• Proactively contribute in developing IoT use cases for industrial customers
• MS or PhD in a quantitative discipline: Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering, Economics, etc.
• 2-5 years of experience in a relevant role.
• Experience collaborating with Developers, UX Experts, Product Owners, etc.
• Advance working SQL knowledge and any relational databases
• Working experience with cloud services such as AWS or Azure.
• Experience in performing offline visual analytics with BI Tools like Tableau/Qlik Sense is preferred
• Excellent analytical skills and building processes supporting data transformation, data structures, metadata, dependency and workload management
• Closely follow latest developments in artificial intelligence and be an early adopter of disruptive trends/technologies
• Proven deep technical expertise in designing and programming large scale data driven solutions using any of the high-level programming languages viz. Java/Python/C++/Scala
• Hands-on experience with one or more of the following is a must:
o Building analytical solutions leveraging distributed computing frameworks and cloud-based data engineering solutions, especially Hadoop and Spark to build and deploy products at scale
o Experience in Machine Learning techniques such as Forecasting, Classification, Clustering, Text Mining, Decision Trees, Random Forest and Search algorithms
o Understanding and hands-on experience in training deep convolutional and/or recurrent networks using frameworks like Tensorflow, Caffe, MXNet, etc.
o Research, develop and prototype software technologies related to object detection, tracking, 3D reconstruction, SLAM and photometric stereo
o Understanding and hand-on experience of designing and modeling industrial optimization problems
o 1-2 years’ experience working on image processing and computer vision problems with a clear understanding and ability to implement algorithms (especially deep learning algorithms)
o Hands-on experience using OpenCV and OpenGL
o Optimization techniques for model training and deployment on GPUs
• Experience working with big data architecture and data sets
• Strong working knowledge of Python, Hadoop. Spark, etc.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basic of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Organization: Digital Industries
Company: Siemens Pte Ltd
Experience Level: Mid-level Professional
Job Type: Full-time