Data Scientist

**votre rôle**:

- Work with product, Technical and Customer Support on identifying problems in different areas where machine learning/statistics can help.
- Present your findings to both technical and non-technical audiences.
- Lead traversal technical discussion with CTIO and GDO team
- Participate in the entire LLM development lifecycle, from problem definition and data preparation to model training, evaluation, and deployment.
- Identify and own Use case coming from Different BU and lead it from doing POC till industrialization
- Design and implement novel LLM architectures and training techniques, leveraging deep learning frameworks like TensorFlow and PyTorch.
- Build and share technical architecture with Business SPOC
- Develop and maintain efficient pipelines for data preprocessing, training, and inference.
- Collaborate with data scientists to curate, clean, and prepare high-quality training data for LLMs.
- Develop, contribute and Lead technical framework for AI use case environment
- Evaluate LLM performance using appropriate metrics and identify opportunities for improvement.
- LLM performance evaluation and define metrices
- help and act as Technical support to Other DS within team.
- Act as technical consultant for Data & AI use case.
- Deploy LLMs to production environments and monitor their performance for accuracy, fairness, and efficiency
- help and act as Technical support to Other DS within team.
- Act as technical consultant for Data & AI use case.
- Lead and Research and development initiative within Team and come up industry standard practice within team to follow in ongoing projects.
- Stay up-to-date on the latest advancements in LLM research and identify opportunities to incorporate them into your work
- Act as SPOC from data & AI team to leverage AI capabilities with Business team.
- Build scalable back-end systems and APIs to support AI model integration and data processing using languages such as Python, Java, or Node.js.
- Define Solution design proposal for business
- Manage the infrastructure needed for data scientists to run their experiments and deploy models. This includes setting up compute clusters with GPUs or TPUs for computationally intensive tasks, configuring cloud storage for datasets, and managing containerized environments for model deployment Document your work clearly and concisely, including research papers, technical reports, and code documentation.

**votre profil**:

- A minimum of 5 years’ hands-on applied research experience developing and implementing machine learning models on large scale data sets
- Expert proficiency interrogating distributed databases (Map/Reduce, Hadoop, Hive, ) is also highly desired should have worked on handling web automation projects.
- Positive work attitude with ability to work well as a “team” with personnel from other teams/organizations.
- Have extensive experience of the design and delivery of AI or Big Data solutions
- Expertise in machine learning and statistical analysis approaches such as classification, clustering, regression, statistical inference, collaborative filtering, natural
- Hands-on experience in conducting analyses on unstructured structured / semi-structured data
- Ability to drive initiatives from within the team and realize them for organization and/or team’s benefit. Excellent interpersonal and communication skills.

**le plus de l'offre**:

- Advanced Analytics & Data Science, Machine Learning, Deep Learning and Automation.
- Knowledge of R, SQL and Python; familiarity with Scala, Java. Working knowledge of diverse API’s

**contrat**:
CDI