3 Gnns Jobs

Role & responsibilities Job Description: As a Senior Data Scientist specializing in Fraud and Anomaly Detection , you will play a pivotal role in developing and implementing advanced models and algorithms to detect fraudulent activities and anomalies within complex datasets. You will leverage your expertise in Graph Anomaly Detection and Graph Neural Networks to enhance our anomaly detection capabilities. Key Responsibilities: o Experience: o Candidate must have hands-on experience in the field of Anomaly/Fraud/Outlier Detection and the connected technologies. Advanced Model Development and Implementation: o Design and develop sophisticated machine learning models specifically tailored for fraud and anomaly detection using state-of-the-art techniques. Implement Graph Neural Networks (GNNs) using PyTorch Geometric to capture complex relationships and dependencies in graph-structured data. o Develop algorithms for Graph Anomaly Detection, focusing on node, edge, and subgraph anomaly identification. o Have a know-how of various Graph Neural Network algorithms like GCN, GraphSAGE, GAT, GINE, Graph Message Passing, Hetero Graph Learning etc. o Have very good understanding and know how of various NLP techniques and models like BERT, Text Classification, Text Extraction, LLMs and LLM Fine-Tuning etc. Data Analysis and Feature Engineering: o Perform in-depth exploratory data analysis (EDA) to uncover hidden patterns and insights related to fraudulent activities. o Engineer features from large-scale datasets, including constructing graph-based features such as node embeddings, edge attributes, and graph metrics. Utilize techniques such as spectral clustering and community detection to enhance feature representation in graph data. o Utilize various nlp techniques for data cleaning, visualization, data pre-processing and data preparation o Research and Innovation: o Conduct cutting-edge research on emerging methodologies in fraud detection, anomaly detection, and graph-based machine learning. INTERNAL o Experiment with novel architectures and algorithms, such as attention mechanisms in GNNs, to improve detection capabilities. o Prototype and evaluate new approaches using rigorous experimental design and statistical validation. o Collaboration and Communication: o Work closely with other data scientists to ensure seamless integration of models into production environments, optimizing for scalability and performance. o Collaborate with software developers to implement efficient data pipelines and real-time processing systems. Technical Skills: o Programming and Libraries o Proficiency in Python, with extensive experience in libraries such as PyTorch, PyTorch Geometric, TensorFlow, Keras, Transformers and Scikit-learn. o Candidate must know how to work with Graph Based Machine Learning and NLP. o Experience with graph processing frameworks and libraries, such as NetworkX and DGL (Deep Graph Library). o Machine Learning and Deep Learning: o Strong understanding of machine learning algorithms, including supervised, unsupervised, and semi-supervised learning techniques. o Expertise in deep learning architectures, particularly those applicable to graph data, such as Graph Neural Networks like GAT, GraphSAGE etc Preferred candidate profile

As a Principal AI Research Scientist at Siemens Energy, you will lead the research efforts in developing next-generation foundation models for power grid & substation applications. You will play a crucial role in defining the research agenda, driving innovation in algorithms, architectures, and methodologies for scaling AI models, and building and mentoring a world-class team. Your work will have a significant real-world impact on grid modernization, renewable energy acceleration, and climate change mitigation. Your responsibilities will include conducting original, high-impact research in foundation models tailored for power grid data, spearheading the development of novel deep learning architectures and algorithms, and collaborating closely with AI engineers and domain experts to translate research breakthroughs into scalable solutions for real-world power grid systems. You will also build and mentor a high-performing research team, drive rapid iteration and proof-of-concept development, and effectively communicate research findings to technical and non-technical audiences. To qualify for this role, you should have a Master's or Ph.D. in Computer Science, Electrical Engineering, Physics, Mathematics, or a related quantitative field, with 7+ years of research experience in deep learning, machine learning, or related areas. You should have publications in top-tier AI conferences and journals, expertise in building foundation models, and experience with AI for Simulation, GNNs, Transformers, or Diffusion Models. Strong communication, presentation, and teamwork skills are essential, along with the ability to lead and inspire a research team and collaborate effectively with diverse stakeholders. Desired skills include contributions to open-source machine learning projects, prior experience in a top AI research lab, and experience applying AI/ML to power systems, electrical grids, or related domains. If you are a visionary individual with a passion for driving innovation in AI and a desire to revolutionize the energy landscape, we encourage you to apply for this challenging and future-oriented role at Siemens Energy.,

You will be responsible for owning the full ML stack that is capable of transforming raw dielines, PDFs, and e-commerce images into a self-learning system that can read, reason about, and design packaging artwork. This includes building data-ingestion & annotation pipelines for SVG/PDF to JSON conversion, designing and modifying model heads using technologies such as LayoutLM-v3, CLIP, GNNs, and diffusion LoRAs, training & fine-tuning on GPUs, as well as shipping inference APIs and evaluation dashboards. Your daily tasks will involve close collaboration with packaging designers and a product manager, establishing you as the technical authority on all aspects of deep learning within this domain. Your key responsibilities will be divided into three main areas: **Area Tasks:** - Data & Pre-processing (40%): Writing robust Python scripts for parsing PDF, AI, SVG files, extracting text, colour separations, images, and panel polygons. Implementing tools like Ghostscript, Tesseract, YOLO, and CLIP pipelines. Automating synthetic-copy generation for ECMA dielines and maintaining vocabulary YAMLs & JSON schemas. - Model R&D (40%): Modifying LayoutLM-v3 heads, building panel-encoder pre-train models, adding Graph-Transformer & CLIP-retrieval heads, and running experiments, hyper-param sweeps, ablations to track KPIs such as IoU, panel-F1, colour recall. - MLOps & Deployment (20%): Packaging training & inference into Docker/SageMaker or GCP Vertex jobs, maintaining CI/CD, experiment tracking, serving REST/GraphQL endpoints, and implementing an active-learning loop for designer corrections. **Must-Have Qualifications:** - 5+ years of Python experience and 3+ years of deep-learning experience with PyTorch, Hugging Face. - Hands-on experience with Transformer-based vision-language models and object-detection pipelines. - Proficiency in working with PDF/SVG tool-chains, designing custom heads/loss functions, and fine-tuning pre-trained models on limited data. - Strong knowledge of Linux, GPU, graph neural networks, and relational transformers. - Proficient in Git, code review discipline, and writing reproducible experiments. **Nice-to-Have:** - Knowledge of colour science, multimodal retrieval, diffusion fine-tuning, or packaging/CPG industry exposure. - Experience with vector search tools, AWS/GCP ML tooling, and front-end technologies like Typescript/React. You will own a tool stack including DL frameworks like PyTorch, Hugging Face Transformers, torch-geometric, parsing/CV tools, OCR/detectors, retrieval tools like CLIP/ImageBind, and MLOps tools such as Docker, GitHub Actions, W&B or MLflow. In the first 6 months, you are expected to deliver a data pipeline for converting ECMA dielines and PDFs, a panel-encoder checkpoint, an MVP copy-placement model, and a REST inference service with a designer preview UI. You will report to the Head of AI or CTO and collaborate with a front-end engineer, a product manager, and two packaging-design SMEs.,