As an Associate - Data ML Engineer at Bain & Company, you will be part of a global consultancy firm that collaborates with ambitious change makers to redefine industries and achieve extraordinary results. You will work in the Bain Capability Network (BCN) which supports case teams with analytics and research solutions across various industries and domains. **Role Overview:**
As an Associate - Data ML Engineer, your responsibilities will include:
Building and maintaining scalable data pipelines and data processing workflows to support analytics and machine learning use cases. - Developing and deploying machine learning models into production systems, ensuring reliability and scalability. - Designing and implementing backend APIs to serve data products and ML outputs to downstream applications. - Developing lightweight frontend applications, dashboards, or interfaces to enable business users to interact with data and models. - Collaborating with data scientists to productionize models and integrate them into end-to-end solutions. - Working with large-scale datasets across cloud-based data platforms and warehouses. - Implementing best practices in software engineering, including modular code design, testing, and version control. - Utilizing containerization (Docker) and CI/CD pipelines for efficient deployment and scaling of applications. - Optimizing performance of data pipelines, APIs, and user-facing applications. - Translating business requirements into technical solutions and supporting stakeholders with data-driven tools and insights. **Key Responsibilities:**
Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related technical fields. - 25 years of experience in data engineering, ML engineering, or full-stack development roles. - Strong programming skills in Python and SQL, with experience building data pipelines and backend services. - Hands-on experience with machine learning model integration and deployment in production environments. - Experience developing backend APIs using frameworks such as FastAPI, Flask, or Django. - Exposure to frontend technologies (e.g., React, NextJs or similar) to build user-facing applications and dashboards. - Experience working with cloud platforms (AWS/Azure/GCP) and modern data architectures (data lakes, warehouses). - Familiarity with CI/CD pipelines, Docker, and version control (Git). - Understanding of data modeling, ETL processes, and scalable system design. - Ability to work across the stackfrom data pipelines to APIs to front-end interfaces. - Good-to-have: Proficiency in Excel and PowerPoint with ability to support business communication and storytelling. - Good-to-have: Prior exposure to consulting, analytics use cases, or Consumer Products domain. In your role, you will collaborate with the Consumer Products Center of Expertise to deliver advanced analytics solutions to Bain's clients in the Consumer Products sector. You will work with a mix of Directors, Managers, Project Leads, Associates, and Analysts to implement cloud-based end-to-end analytics solutions and support various projects within the CP ringfence or other industry CoEs. As an Associate - Data ML Engineer, you are expected to have a knack for seeking out challenging problems, producing your own ideas, and brainstorming with peers and managers. You should be willing to learn new techniques, solve problems with an interdisciplinary approach, and demonstrate excellent coding skills by writing modular, reusable, and functional code. As an Associate - Data ML Engineer at Bain & Company, you will be part of a global consultancy firm that collaborates with ambitious change makers to redefine industries and achieve extraordinary results. You will work in the Bain Capability Network (BCN) which supports case teams with analytics and research solutions across various industries and domains. **Role Overview:**
As an Associate - Data ML Engineer, your responsibilities will include:
Building and maintaining scalable data pipelines and data processing workflows to support analytics and machine learning use cases. - Developing and deploying machine learning models into production systems, ensuring reliability and scalability. - Designing and implementing backend APIs to serve data products and ML outputs to downstream applications. - Developing lightweight frontend applications, dashboards, or interfaces to enable business users to interact with data and models. - Collaborating with data scientists to productionize models and integrate them into end-to-end solutions. - Working with large-scale datasets across cloud-based data platforms and warehouses. - Implementing best practices in software engineering, including modular code design, testing, and version control. - Utilizing containerization (Docker) and CI/CD pipelines for efficient deployment and scaling of applications. - Optimizing performance of data pipelines, APIs, and user-facing applications. - Translating
