About the role
We're building a talent pipeline of Data Scientists and AI Specialists for upcoming opportunities and projects with our clients.
We're looking for professionals who can transform data and business needs into practical, high-impact solutions using Data Science, Machine Learning, and Artificial Intelligence. Depending on the project, you'll have the opportunity to work across different stages of the AI lifecycle, from data exploration and model development to deployment, monitoring, and continuous improvement in production environments.
Projects may involve traditional Machine Learning, predictive analytics, Generative AI, Large Language Models, and advanced AI applications. We're interested in professionals with strong technical foundations, analytical thinking, and the ability to collaborate with technical teams and business stakeholders.
Responsibilities
- Analyze structured and unstructured data to identify patterns, trends, and business opportunities.
- Design, develop, train, and evaluate Machine Learning and AI models.
- Build solutions for predictive analytics, classification, segmentation, recommendation, forecasting, and other Data Science use cases.
- Contribute to Generative AI, Large Language Models (LLMs), and Natural Language Processing (NLP) projects.
- Develop AI solutions using techniques such as embeddings, semantic search, Retrieval-Augmented Generation (RAG), and prompt engineering.
- Prepare, clean, transform, and validate data for model training and evaluation.
- Conduct exploratory data analysis and statistical experimentation.
- Define metrics and methodologies to assess model performance, quality, and reliability.
- Collaborate with Data Engineers, Software Engineers, MLOps Engineers, Product Teams, and business stakeholders.
- Support the deployment and integration of Machine Learning and AI models into production applications.
- Monitor model performance and identify issues related to quality, drift, reliability, and data integrity.
- Communicate technical findings, results, and recommendations to technical and non-technical audiences.
- Document models, experiments, methodologies, and implemented solutions.
- Research and evaluate emerging technologies, tools, and methodologies in Data Science and AI.
Requirements
- Degree in Computer Science, Systems Engineering, Statistics, Mathematics, Physics, Data Science, Engineering, or a related field, or equivalent practical experience.
- Professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, AI Specialist, or in a related role.
- Hands-on experience developing Machine Learning models and/or AI solutions.
- Strong knowledge of Python and tools used for data analysis and modeling.
- Understanding of statistics, Machine Learning techniques, and data analysis.
- Experience processing, transforming, and analyzing data.
- Knowledge of databases and SQL.
- Experience with relevant Data Science and Machine Learning libraries or frameworks.
- Ability to select and apply appropriate metrics to evaluate models according to the business problem.
- Understanding of the Machine Learning development and deployment lifecycle.
- Strong analytical, critical-thinking, and problem-solving skills.
- Ability to translate business needs into data-driven and AI-based solutions.
- Strong communication and collaboration skills in multidisciplinary environments.
- English proficiency sufficient to participate in meetings and collaborate with international teams and/or clients.
Nice to have
- Experience developing Generative AI solutions and working with Large Language Models (LLMs).
- Knowledge of RAG, embeddings, vector databases, and prompt engineering.
- Experience in NLP, Computer Vision, forecasting, recommendation systems, or other specialized AI fields.
- Familiarity with frameworks such as Scikit-learn, TensorFlow, PyTorch, or equivalent tools.
- Experience with cloud platforms and Data/AI services.
- Knowledge of MLOps, model versioning, ML pipelines, and automation.
- Experience deploying models through APIs, cloud services, or microservice architectures.
- Understanding of model monitoring, model drift, data drift, and data quality.
- Experience with data architectures, Data Lakes, Data Warehouses, or Big Data platforms.
- Knowledge of Responsible AI, security, privacy, and data governance practices.
- Experience working with Agile or Scrum methodologies.
- Relevant certifications in Data Science, Machine Learning, Artificial Intelligence, or cloud platforms.