Data Scientist / Machine Learning Engineer (M/F) - 2 positions

THE COMPANY:

ExcellerIA is a French Deeptech company developing its AI engine Axolotl. It is a predictive analysis platform (Predictive Layer) that allows organizations, from midmarket to large accounts, to have accurate and quickly accessible prediction data. ExcellerIA has its own AI research laboratory in Lyon, as well as a recent sales agency in San Diego, with headquarters in Montpellier.

Axolotl is the platform that brings together a modeling core (prediction-framework), a data pipeline (BeeStack), its job configuration interface (Wizard), and a supervision and dashboarding tool (IRMA). The platform must evolve toward an appliance, for which you will support the development. The architecture is datacentric, allowing for distributed processing on volumes on the order of a terabyte, split into several clusters in the mathematical sense and other datasets.

THE POSITION:

Reporting to the Lyon laboratory, within a small team, you will participate in several data science activities. From research, prototyping, development, and the implementation of AI models. Your work will focus on the development of data flows, and then subsequently the processing and configuration of AI models. This position is evolvable within 1 or 2 years.

You will also participate in certain client meetings.

A global understanding of the entire chain is indispensable to succeed in this position, as is reasoning about architecture before reasoning about the model.

SEARCHED PROFILE:

Engineering degree, renowned school. 5 years of experience in data science or machine learning engineering, including a significant portion on models moved into production. Mastery of at least 2 languages, including Python. Culture and appetite for architectures, ability to grasp complex and distributed applications. Rigor and method, as our performance commitments are contractual. English C1 minimum: we work with teams in the United States.

ASSETS for the position:

Having programmed massively parallel data stacks, processed big-data, understanding how neural networks work, knowing datacentric approaches, time series, understanding the concepts of predictive and semantic layer. Solving equations to relax.

TECHNICAL ENVIRONMENT:

Python, PyTorch, TensorFlow, Scikit-learn, SQL, MongoDB, all the standard developer and datascientist tooling.