Data Scientist

Data Scientist You will join a highly innovative industrial environment where manufacturing processes achieve extreme precision (up to 10 microns) through advanced machining and forging technologies. As a Data Scientist , you will collaborate with Manufacturing 4.0, IT, and operational teams to develop and deploy Machine Learning and/or Computer Vision solutions tailored to industrial needs. You will work across the full lifecycle: data exploration, model design, training, validation, deployment, monitoring, and continuous improvement. You will be involved in building the future of Manufacturing 4.0, leveraging data‑driven factory control, robotics, and artificial intelligence. This multidisciplinary department includes specialists in: IT platforms Manufacturing Execution Systems Unified Namespace (UNS) and real‑time data distribution Quality inspection systems Data disciplines (BI, ML, AI, data engineering) Key Responsibilities Explore, clean, and analyze industrial data from machines, sensors, MES, and quality inspection systems. Design, train, and evaluate ML and Computer Vision models suitable for industrial constraints. Build end‑to‑end MLOps pipelines (training, validation, deployment, performance monitoring). Deploy models in production using a hybrid on‑prem + cloud architecture leveraging containerization and AWS SageMaker . Monitor model performance over time (drift, robustness, data quality) and propose improvements. Integrate models within existing systems and CI/CD workflows alongside IT teams. Document solutions and share best practices within the team. The team operates in Agile mode (sprints, reviews, daily meetings). Key Challenges Be a central contributor to the Manufacturing 4.0 transformation through data & AI. Deliver robust, reliable, maintainable AI models in a demanding industrial context. Balance innovation with production constraints. Demonstrate autonomy, critical thinking, and proactive problem‑solving. Strengthen and promote MLOps practices across the data team. Required Profile Engineering degree in data, computer science, applied mathematics, AI, or equivalent. 5+ years of experience as a Data Scientist or Machine Learning Engineer, ideally in industrial or production‑critical environments. Proven experience designing, training, validating, and deploying ML/AI models in production. Strong command of MLOps best practices : Data/model versioning Pipeline automation CI/CD Model lifecycle management Performance and drift monitoring Experience with hybrid infrastructure (on‑prem + cloud) and AWS , especially SageMaker . Ability to collaborate with cross‑functional teams (IT, data, industrial experts) and convert business needs into technical solutions. Knowledge of industrial environments, OT systems, or MES data is a plus. Strong interpersonal skills, autonomy, initiative, and rigour. Required Technical Skills Machine Learning & AI Supervised & unsupervised ML (regression, classification, anomaly detection, time series). Computer Vision (image processing, CNNs, deep learning) or predictive industrial models. Programming & Frameworks Python, Pandas, NumPy, Scikit‑learn PyTorch and/or TensorFlow Deployment & MLOps Model serving: APIs, batch, streaming MLOps pipeline engineering Version control (code, data, models) Docker / Podman Kubernetes / OpenShift AWS services: S3, SageMaker, IAM, ECR, CloudWatch Git, CI/CD, testing best practices Nice‑to‑Have Skills Industrial manufacturing experience (MES, quality, predictive maintenance). Industrial data architectures (data lakes, real‑time streaming). Experience with Snowflake or Dataiku . Kafka / MQTT Prometheus, Grafana, CloudWatch Handling large or heterogeneous sensor‑based datasets (time series). Industrial & cloud cybersecurity basics. On‑site presence 2 days/week remote work , depending on workload and objectives. French mandatory. English for technical documentation. #J-18808-Ljbffr