| Data Science Solutions Architect- Local to Santa Clara, CA at Santa Clara, California, USA |
| Email: [email protected] |
| From: Sandeep Bisht, Key Infotek [email protected] Reply to: [email protected] Role: Data Science Solutions Architect Job Location: Santa Clara, CA Interview: 1st Level Video Interview and 2nd Level In-Person Work Type: Hybrid Model (3 days remote and 2 days onsite) Only local candidates to Santa Clara, CA who can work 2 days onsite every week Role: This candidate should be able to facilitate adoption of ML/AI across business units within the organization. Engage with global teams to effectively translate customer requirements to software requirements and identify potential solutions that are optimized to operate with in constraints of the product. Provide timely status updates and proactive communications on issues to the stakeholders. Perform analysis, design, and implementation of assigned projects using sound engineering principles and adhering to business standards, practices, processes, and product/program requirements. Must haves: Excellent communication skills Ability to advocate ML/AI capabilities across different business units within the organization. Strong problem-solving skills and ability to quickly learn new complex processes/systems such as semiconductor manufacturing. Ability to quickly understand/identify process issues based on inputs from domain experts and be able to formulate problem statements for data scientists. Primary responsibility will be to develop and optimize custom ML/AI algorithms for new and existing applications. Broad knowledge of computer vision, NLP, time series forecasting, anomaly detection Knowledge of traditional ML algorithms such as, regression, classification, and clustering algorithms Knowledge of state-of-the-art deep learning model architectures in the areas of computer vision (NLP would be a plus) Experience in implementing and optimizing object detection and instance/semantic segmentation models. Experience in setting up end-to-end pipelines for model deployment. Experience in model performance tracking using appropriate KPIs. Strong fundamentals in Python programming Good knowledge of OpenCV, Scikit-image, TensorFlow, Torch, Pillow, numpy, pandas, scikit-learn etc. Understanding of SW development cycle, from requirements to testing, integration and delivery Familiarity with model shrinking techniques for deployment on edge devices with limited footprint. Nice to have Experience in process improvement in manufacturing industries using ML/AI Experience in defect identification and root cause analysis in manufacturing domain Keywords: artificial intelligence machine learning California |
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| Sat Jan 21 03:15:00 UTC 2023 |