Job
Description
As an Experienced Data Scientist at our company, your role will involve developing and implementing Generative AI solutions to address critical business challenges and unlock new opportunities. You will leverage your expertise in data science, machine learning, and Generative AI techniques to drive impactful projects from ideation to deployment. **Key Responsibilities:** - **Data Handling & Preparation**: - Work with diverse and large-scale datasets. - Perform data cleaning, preprocessing, and feature engineering. - Create synthetic datasets as needed for training and evaluating Generative AI models. - Ensure data quality and integrity throughout the model development lifecycle. - **Model Optimization & Deployment**: - Optimize Generative AI models for performance, scalability, and efficiency. - Consider responsible AI practices such as bias detection and mitigation. - Collaborate with Machine Learning Engineers and DevOps teams to deploy models into production environments. - **Prompt Engineering & Fine-tuning**: - Develop effective prompts and fine-tuning strategies for Large Language Models. - Adapt models to specific domains and tasks to achieve desired outputs. - **Generative AI Model Development & Implementation**: - Design, develop, train, fine-tune, and deploy various Generative AI models. - Models may include Large Language Models, diffusion models, GANs, VAEs. - Applications include text generation, image and video synthesis, code generation, and synthetic data generation. - **Research & Innovation**: - Stay updated with the latest research in Generative AI. - Explore new architectures, methodologies, and tools. - Identify opportunities to apply cutting-edge techniques to solve business problems. - **Experimentation & Evaluation**: - Design and execute experiments to evaluate model performance. - Analyze results using metrics like BLEU, ROUGE, FID, perplexity. - Iterate on model development based on findings. - **Collaboration & Communication**: - Work closely with product managers, engineers, researchers, and stakeholders. - Understand requirements, communicate technical findings, and translate AI concepts for non-technical audiences. - **Documentation & Knowledge Sharing**: - Maintain comprehensive documentation of development processes, models, and results. - Share knowledge and best practices with the data science and engineering teams. In addition to the responsibilities mentioned, you will adhere to ethical AI principles and guidelines to ensure fairness, transparency, and accountability in the development and deployment of Generative AI models. As an Experienced Data Scientist at our company, your role will involve developing and implementing Generative AI solutions to address critical business challenges and unlock new opportunities. You will leverage your expertise in data science, machine learning, and Generative AI techniques to drive impactful projects from ideation to deployment. **Key Responsibilities:** - **Data Handling & Preparation**: - Work with diverse and large-scale datasets. - Perform data cleaning, preprocessing, and feature engineering. - Create synthetic datasets as needed for training and evaluating Generative AI models. - Ensure data quality and integrity throughout the model development lifecycle. - **Model Optimization & Deployment**: - Optimize Generative AI models for performance, scalability, and efficiency. - Consider responsible AI practices such as bias detection and mitigation. - Collaborate with Machine Learning Engineers and DevOps teams to deploy models into production environments. - **Prompt Engineering & Fine-tuning**: - Develop effective prompts and fine-tuning strategies for Large Language Models. - Adapt models to specific domains and tasks to achieve desired outputs. - **Generative AI Model Development & Implementation**: - Design, develop, train, fine-tune, and deploy various Generative AI models. - Models may include Large Language Models, diffusion models, GANs, VAEs. - Applications include text generation, image and video synthesis, code generation, and synthetic data generation. - **Research & Innovation**: - Stay updated with the latest research in Generative AI. - Explore new architectures, methodologies, and tools. - Identify opportunities to apply cutting-edge techniques to solve business problems. - **Experimentation & Evaluation**: - Design and execute experiments to evaluate model performance. - Analyze results using metrics like BLEU, ROUGE, FID, perplexity. - Iterate on model development based on findings. - **Collaboration & Communication**: - Work closely with product managers, engineers, researchers, and stakeholders. - Understand requirements, communicate technical findings, and translate AI concepts for non-technical audiences. - **Documentation & Knowledge Sharing**: - Maintain comprehensive documentation of development processe
