about the company resources is the backbone of publicis groupe, the world's third-largest communications group. formed in 1998 as a small team to service a few publicis groupe firms, re:sources has grown to 5,000+ people servicing a global network of prestigious advertising, public relations, media, healthcare, and digital marketing agencies. we provide technology solutions and business services including finance, accounting, legal, benefits, procurement, tax, real estate, treasury, and risk management to help publicis groupe agencies do their best: create and innovate for their clients. in addition to providing essential, everyday services to our agencies, re:sources develops and implements platforms, applications, and tools to enhance productivity, encourage collaboration, and enable professional and personal development. we continually transform to keep pace with our ever-changing communications industry and thrive on a spirit of innovation felt around the globe. with our support, publicis groupe agencies continue to create and deliver award-winning campaigns for their clients. about the role the main purpose of this role is to advance the application of business intelligence, advanced data analytics, and machine learning for marcel. the data scientist will work with other data scientists, engineers, and product owners to ensure the delivery of all commitments on time and in high quality. responsibilities design and develop advanced data science and machine learning algorithms, with a strong emphasis on natural language processing (nlp) for personalized content, user understanding, and recommendation systems. work on end-to-end llm-driven features, including fine-tuning pre-trained models (e.g., bert, gpt), prompt engineering, vector embeddings, and retrieval-augmented generation (rag). build robust models on diverse datasets to solve for semantic similarity, user intent detection, entity recognition, and content summarization/classification. analyze user behaviour through data and derive actionable insights for platform feature improvements using experimentation (a/b testing, multivariate testing). architect scalable solutions for deploying and monitoring language models within platform services, ensuring performance and interpretability. collaborate cross-functionally with engineers, product managers, and designers to translate business needs into nlp/ml solutions. regularly assess and maintain model accuracy and relevance through evaluation, retraining, and continuous improvement processes. write clean, well-documented code in notebooks and scripts, following best practices for version control, testing, and deployment. communicate findings and solutions effectively across stakeholders - from technical peers to executive leadership. contribute to a culture of innovation and experimentation, continuously exploring new techniques in the rapidly evolving nlp/llm space. qualifications minimum experience (relevant): 3 years maximum experience (relevant): 5 years required skills proficiency in python and nlp frameworks: spacy, nltk, hugging face transformers, openai, langchain. strong understanding of llms, embedding techniques (e.g., sbert, faiss), rag architecture, prompt engineering, and model evaluation. experience in text classification, summarization, topic modeling, named entity recognition, and intent detection. experience deploying ml models in production and working with orchestration tools such as airflow, mlflow. comfortable working in cloud environments (azure preferred) and with tools such as docker, kubernetes (aks), and git. strong experience working with data science/ml libraries in python (scipy, numpy, tensorflow, scikit-learn, etc.) strong experience working in cloud development environments (especially azure, adf, pyspark, databricks, sql) experience building data science models for use on front end, user facing applications, such as recommendation models experience with rest apis, json, streaming datasets understanding of graph data, neo4j is a plus strong understanding of rdbms data structure, azure tables, blob, and other data sources understanding of jenkins, ci/cd processes using git, for cloud configs and standard code repositories such as adf configs and databricks preferred skills bachelor's degree in engineering, computer science, statistics, mathematics, information systems, or a related field from an accredited college or university; master's degree from an accredited college or university is preferred. or equivalent work experience. advanced knowledge of data science techniques, and experience building, maintaining, and documenting models advanced working sql knowledge and experience working with relational databases, query authoring (sql) as well as working familiarity with a variety of databases preferably graph db. experience building and optimizing adf and pyspark based data pipelines, architectures and data sets on graph and azure datalake. experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. strong analytic skills related to working with unstructured datasets. build processes supporting data transformation, data structures, metadata, dependency and workload management. a successful history of manipulating, processing and extracting value from large disconnected datasets. strong project management and organizational skills. experience supporting and working with cross-functional teams in a dynamic environment.
