BE - Senior Data Science Engineer

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

• 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