Job
Description
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 role involves working with other data scientists, engineers, and product owners to ensure the delivery of all commitments on time and in high quality. Responsibilities Develop and maintain robust Python-based backend services and RESTful APIs to support machine learning models in production. Deploy and manage containerized applications using Docker and orchestrate them using Azure Kubernetes Service (AKS). Implement and manage ML pipelines using MLflow for model tracking, reproducibility, and deployment. Design, schedule, and maintain automated workflows using Apache Airflow to orchestrate data and ML pipelines. Collaborate with Data Scientists to productize NLP models, with a focus on language models, embeddings, and text preprocessing techniques (e.g., tokenization, lemmatization, vectorization). Ensure high code quality and version control using Git; manage CI/CD pipelines for reliable deployment. Handle unstructured text data and build scalable backend infrastructure for inference and retraining workflows. Participate in system design and architecture reviews for scalable and maintainable machine learning services. Proactively monitor, debug, and optimize ML applications in production environments. Communicate technical solutions and project status clearly to team leads and product stakeholders. Qualifications Minimum Experience (relevant): 5 years Maximum Experience (relevant): 9 years 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. Required Skills Proficiency in Python and frameworks like FastAPI or Flask for building APIs. Solid hands-on experience with Docker, Kubernetes (AKS), and deploying production-grade applications. Familiarity with MLflow, including model packaging, logging, and deployment. Experience with Apache Airflow for orchestrating ETL and ML workflows. Understanding of NLP pipelines, language models (e.g., BERT, GPT variants), and associated libraries (e.g., spaCy, Hugging Face Transformers). Exposure to cloud environments, preferably Azure. Strong debugging, testing, and optimization skills for scalable systems. Experience working with large datasets and unstructured data, especially text. Preferred Skills 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. 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. Working knowledge of message queuing, stream processing, and highly scalable Azure based data stores. Strong project management and organizational skills. Experience supporting and working with cross-functional teams in a dynamic environment. Understanding of Node.js is a plus, but not required. Show more Show less
