Lead Data Scientist

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Modality : Hybrid (1 / 2 days per week at the office)

Role Summary

We are seeking a Lead Data Scientist who is passionate about leading innovative projects in artificial intelligence and machine learning. This role requires a combination of advanced technical skills, leadership, and the ability to work in a team. You will guide a team of data scientists and collaborate closely with stakeholders to design solutions that directly impact the company's strategic objectives.

Main Responsibilities

Lead and execute end-to-end Advanced Analytics and Machine Learning projects, following methodologies such as CRISP-DM.

Design, implement, and optimize NLP (Natural Language Processing) and Generative AI models, using advanced frameworks such as PyTorch, TensorFlow, or Hugging Face.

Collaborate with engineering teams to integrate models into production environments using MLOps practices and CI / CD pipelines.

Analyze large volumes of structured and unstructured data using tools such as Python, SQL, and Databricks.

Identify business opportunities through data analysis and generate actionable insights for stakeholders.

Mentor junior profiles within the team, fostering their professional development.

Innovate by proposing new solutions based on emerging technologies such as LLMs (Large Language Models).

Education : Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, Engineering, or related disciplines.

Experience : 3-5 years working as a Data Scientist or Machine Learning Engineer, with at least 1 year in leadership roles.

Advanced proficiency in Python programming and practical knowledge in AI / ML-related frameworks (Scikit-Learn, TensorFlow, PyTorch).

Experience with data processing and analysis tools : SQL, Databricks, and relational / non-relational databases.

Solid knowledge in MLOps practices, continuous integration (CI / CD), and deployment of models in production.

Familiarity with advanced techniques in NLP (pre trained models like BERT / GPT) and generative algorithms.

Experience working in cloud environments (AWS, GCP, or Azure).

Knowledge of vector databases, semantic embeddings, and transformer architectures.

Interpersonal skills to interact with stakeholders at different organizational levels.

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