Senior Data Scientist | Partner Content

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Requirements:

• Bachelor's or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field
• Proficiency in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, or similar
• Knowledge of generative models, including GANs, VAEs, autoregressive models, and transformers
• Strong problem-solving abilities, with a focus on experimental design and data analysis
• Excellent verbal and written communication skills (both in English and Portuguese), with the ability to explain complex technical concepts to non-technical stakeholders.

Responsibilities:

• Identifying opportunities to use GenAI to address business challenges, enhance existing processes, or create innovative solutions.
• Provide data insights and support product decisions leveraging existing data or gather additional data to understand user behavior.
• Fine-tune pre-trained models to adapt to specific use cases and requirements, including performing large language models (LLMs) fine-tuning and optimization for enhanced model performance. Additionally, collaborate with data scientists to annotate and preprocess data for model training, ensuring the models are tailored effectively to the intended applications.
• Develop and refine prompts to enhance the performance and output quality of generative models, ensuring that the models respond accurately and relevantly to various input scenarios.
• Collect, preprocess, and analyze large datasets to ensure high-quality input data for training generative models.
• Develop and utilize embeddings to represent complex data types (e.g., text, images) in a way that facilitates the generation of meaningful and accurate outputs.
• Stay current with the latest research in generative AI, propose new approaches, and contribute to the academic and technical community through publications and presentations.
• Work closely with cross-functional teams to integrate generative models into products and services, ensuring they meet business objectives and user needs.
• Develop and implement rigorous testing and evaluation frameworks to assess model performance, robustness, and reliability. Collaborate with the Data team in order to improve our internal ML Ops platform.
• Maintain comprehensive documentation of models, algorithms, experiments, and processes to ensure reproducibility and knowledge sharing.