Mid-level Data Scientist

NEORIS is a Digital accelerator that helps companies enter the future, having 20 years of experience as Digital Partners of some of the largest companies in the world. We have more than 4,000 professionals in 11 countries, with our multicultural startup culture where we cultivate innovation, continuous learning to create high-value solutions for our clients. We are looking for a motivated Mid level Data Scientist with experience in Python, statistical analysis, and machine learning to support the development of data-driven and AI-powered solutions. In this role, you will collaborate with senior data scientists, engineers, and business stakeholders to analyze data, build predictive models, and contribute to scalable analytics and AI applications. This is a great opportunity for a data professional to grow their expertise in Machine Learning, Generative AI, and modern data science practices while working on real-world business problems. You will gain hands-on experience building models and applications that support production environments. Key Responsibilities

• Model Development: Support the implementation of machine learning models, assisting with data preprocessing, feature engineering, model training, and evaluation. - Statistical Analysis: Apply techniques such as regression, classification, clustering, and anomaly detection to solve business and operational problems. - AI Innovation: Assist in developing applications using Generative AI (LLMs, prompt engineering, embeddings, or Retrieval-Augmented Generation). - Integration: Work with APIs and backend services to integrate ML and AI models into internal systems. - Workflow Optimization: Support the development of scalable ML workflows and data pipelines for experimentation and training. - Cross-functional Collaboration: Partner with data engineers, DevOps and software developers to support the deployment and monitoring of models. - Code Quality: Write clean, well-documented code using Python and SQL. - Data Storytelling: Prepare visualizations and reports to communicate insights and results to both technical and non-technical stakeholders.