Overview
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
1. Data Science and Machine Learning
1. Design and develop advanced machine learning models for various use cases including predictive analytics, recommendation systems, natural language processing, and computer vision
2. Conduct thorough data exploration and analysis to identify patterns, trends, and insights
3. Implement feature engineering and selection techniques to optimize model performance
4. Ensure model interpretability and explainability for business stakeholders
1. MLOps and Model Deployment
5. Design and implement end-to-end MLOps pipelines for model training, validation, and deployment
6. Establish automated model monitoring and retraining workflows
7. Implement A/B testing frameworks for model performance evaluation
8. Ensure models meet production requirements for scalability, latency, and reliability
1. Data Engineering and Infrastructure
9. Collaborate with data engineers to design and optimize data pipelines for ML workloads
10. Implement data quality validation and monitoring systems
11. Work with cloud platforms (AWS, Azure, GCP) to deploy scalable ML infrastructure
12. Utilize big data technologies (Spark, Kafka, etc.) for large-scale data processing
1. Solution Architecture and Design
13. Design scalable and robust data science solutions that align with business requirements
14. Architect real-time and batch inference systems for production deployment
15. Implement best practices for model versioning, experiment tracking, and reproducibility
16. Ensure solutions follow security and compliance requirements
1. Leadership and Collaboration
17. Lead cross-functional project teams including data scientists, engineers, and business stakeholders
18. Mentor junior data scientists and promote knowledge sharing within the team
19. Collaborate with clients to understand business requirements and translate them into technical solutions
20. Drive innovation and adoption of new tools, techniques, and methodologies
Qualifications
Required Skills and Experience
Technical Skills
21. Machine Learning: Deep expertise in supervised and unsupervised learning, deep learning frameworks (TensorFlow, PyTorch), and model optimization techniques
22. Programming: Strong proficiency in Python and/or R, with experience in SQL and knowledge of additional languages (Java, Scala) as a plus
23. Data Engineering: Experience with data pipeline tools (Airflow, Prefect), big data technologies (Spark, Kafka), and data warehousing concepts
24. MLOps: Hands-on experience with MLOps tools (MLflow, Kubeflow, Sagemaker) and model deployment strategies (Docker, Kubernetes)
25. Cloud Platforms: Proficiency with cloud-based ML services (AWS SageMaker, Azure ML) and infrastructure management
26. Statistical Analysis: Strong foundation in statistics, experimental design, and hypothesis testing
27. Agentic AI: Interest or experience with agentic Artificial intelligence frameworks and multi-agent systems (LangChain, AutoGen, CrewAI, etc.) is a plus
Experience
28. Minimum of 5 years of experience in data science and machine learning, with at least 2 years in a senior or lead role
29. Proven track record of deploying machine learning models in production environments
30. Experience with end-to-end data science project delivery in enterprise environments
31. Strong understanding of software development best practices and agile methodologies
Soft Skills
32. Excellent communication skills, both written and verbal
33. Fluent in French and English (required)
34. Able to travel in Europe for client engagements and project delivery
35. Strong problem-solving abilities and analytical mindset
36. Ability to translate complex technical concepts for business stakeholders
37. Leadership experience with cross-functional teams
Preferred Qualifications
38. Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, or related field
39. Experience with specific industry domains (finance, healthcare, retail, etc.)
40. Knowledge/experience with Databricks, Azure Fabric, or IBM Watson X (a plus)
41. Publications in peer-reviewed conferences or journals
42. Certifications in cloud platforms (AWS Certified Machine Learning, Azure Data Scientist Associate, etc.)
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