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Wavicle Data Solutions | Senior Data Scientist
1 month ago
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Key Responsibilities:
• Generative AI Expertise: Hands-on experience with GenAI models, including prompt engineering and working with commercial models like OpenAI and Anthropic.
• Team Collaboration: Participate in a collaborative and iterative product development process, contributing to team-driven success.
• Data Science Development: Build and implement models using Python, pandas, JSON, and tools like TensorFlow, PyTorch, and Keras.
• Cloud Platforms: Deploy and manage models on Azure (preferred), AWS, or GCP.
• AI for Healthcare: Collaborate with clinical practitioners on Diagnosis Related Group (DRG) use cases and develop AI models to support healthcare decision-making and automation processes.
• Machine Learning: Design and deploy scalable, distributed ML models and perform model optimization, compression, and evaluation.
• NLP and Image Processing: Apply advanced NLP techniques for text processing, entity extraction, and image processing techniques like text detection, OCR, and human/face detection.
• MLOps and Deployment: Utilize MLOps tools (e.g., MLFlow, Kubeflow) and frameworks like Flask or FastAPI for seamless deployment on Docker, Kubernetes, or cloud platforms.
• Statistical Analysis: Conduct exploratory data analysis (EDA), hypothesis testing, regression/classification, and ensemble modeling with strong statistical knowledge.
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Skills and Qualifications:
• Experience: 5+ years in computer vision, object detection, AI, automation, and machine learning.
• Programming Expertise: Proficient in Python, R, SQL, and ML tools like TensorFlow, PyTorch (preferred), and distributed training with Spark.
• Tools & Frameworks: Hands-on experience with CUDA, OpenCL, OpenGL, OpenCV, Flask, FastAPI, Docker, and Kubernetes.
• MLOps: Expertise in tools like MLFlow, Kubeflow, and exporting formats like PMML, Pickle, or ONNX.
• Cloud Platforms: Strong knowledge of deploying AI/ML solutions on Azure, AWS, or GCP.
• AI & ML Concepts: Deep understanding of statistical and ML concepts, algorithms, Bayesian approaches, and evaluation techniques.
• Communication Skills: Excellent written and verbal technical communication skills and a strong team player.
