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.

Key Responsibilities

1. 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

2. 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

3. 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

4. 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

5. 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

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 AI 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
• Leadership experience with cross-functional teams

Preferred Qualifications

• Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, or related field
• Experience with specific industry domains (finance, healthcare, retail, etc.)
• Knowledge/experience with Databricks, Azure Fabric, or IBM Watson X (a plus)
• Publications in peer-reviewed conferences or journals
• Certifications in cloud platforms (AWS Certified Machine Learning, Azure Data Scientist Associate, etc.)