BE Senior Data Science Engineer

Overview We are seeking a highly skilled and experienced

Senior Data Science Engineer

to join our "Data & AI" service line at CBTW. In this role, you will play a critical role in designing, implementing, and deploying advanced data science and machine learning solutions for our European clients. You will work at the intersection of data engineering, machine learning, and software engineering to deliver scalable, production-ready AI solutions.

You will lead end-to-end data science projects, from problem definition and data exploration to model development, deployment, and monitoring. You will collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders to create innovative AI-driven solutions that deliver measurable business value. As a senior member of the team, you will also mentor junior data scientists and drive best practices in MLOps and model lifecycle management.

Responsibilities Key Responsibilities

Data Science and Machine Learning

Design and develop advanced machine learning models for various use cases including predictive analytics, recommendation systems, natural language processing, and computer vision

Conduct thorough data exploration and analysis to identify patterns, trends, and insights

Implement feature engineering and selection techniques to optimize model performance

Ensure model interpretability and explainability for business stakeholders

MLOps and Model Deployment

Design and implement end-to-end MLOps pipelines for model training, validation, and deployment

Establish automated model monitoring and retraining workflows

Implement A/B testing frameworks for model performance evaluation

Ensure models meet production requirements for scalability, latency, and reliability

Data Engineering and Infrastructure

Collaborate with data engineers to design and optimize data pipelines for ML workloads

Implement data quality validation and monitoring systems

Work with cloud platforms (AWS, Azure, GCP) to deploy scalable ML infrastructure

Utilize big data technologies (Spark, Kafka, etc.) for large-scale data processing

Solution Architecture and Design

Design scalable and robust data science solutions that align with business requirements

Architect real-time and batch inference systems for production deployment

Implement best practices for model versioning, experiment tracking, and reproducibility

Ensure solutions follow security and compliance requirements

Leadership and Collaboration

Lead cross‑functional project teams including data scientists, engineers, and business stakeholders

Mentor junior data scientists and promote knowledge sharing within the team

Collaborate with clients to understand business requirements and translate them into technical solutions

Drive innovation and adoption of new tools, techniques, and methodologies

Qualifications Required Skills and Experience

Technical Skills

Machine Learning : Deep expertise in supervised and unsupervised learning, deep learning frameworks (TensorFlow, PyTorch), and model optimization techniques

Programming : Strong proficiency in Python and/or R, with experience in SQL and knowledge of additional languages (Java, Scala) as a plus

Data Engineering : Experience with data pipeline tools (Airflow, Prefect), big data technologies (Spark, Kafka), and data warehousing concepts

MLOps : Hands‑on experience with MLOps tools (MLflow, Kubeflow, Sagemaker) and model deployment strategies (Docker, Kubernetes)

Cloud Platforms : Proficiency with cloud‑based ML services (AWS SageMaker, Azure ML) and infrastructure management

Statistical Analysis : Strong foundation in statistics, experimental design, and hypothesis testing

Agentic AI : Interest or experience with agentic Artificial intelligence frameworks and multi‑agent systems (LangChain, AutoGen, CrewAI, etc.) is a plus

Experience

Minimum of 5 years of experience in data science and machine learning, with at least 2 years in a senior or lead role

Proven track record of deploying machine learning models in production environments

Experience with end‑to‑end data science project delivery in enterprise environments

Strong understanding of software development best practices and agile methodologies

Soft Skills

Excellent communication skills, both written and verbal

Fluent in French and English

(required)

Able to travel in Europe

for client engagements and project delivery

Strong problem‑solving abilities and analytical mindset

Ability to translate complex technical concepts for business stakeholders

Leade