Who you will work with
The Consumer Products Center of Expertise collaborates with Bain’s global Consumer Products Practice leadership, client-facing Bain leadership and teams, and with end clients on development and delivery of Bain’s proprietary CP products and solutions. These solutions aim to answer strategic questions of Bain’s CP clients relating to brand strategy (consumer needs, assortment, pricing, distribution), revenue growth management (pricing strategy, promotions, profit pools, trade terms), negotiation strategy with key retailers, optimization of COGS etc.
You will work as part of the team in CP CoE comprising of a mix of Director, Managers, Projects Leads, Associates and Analysts working to implement cloud-based end-to-end advanced analytics solutions. Delivery models on projects vary from working as part of a CP Center of Expertise, broader global Bain case team within the CP ringfence, or within other industry CoEs such as FS / Retail / TMT / Energy / CME / etc with BCN on need basis
The AS is expected to have a knack for seeking out challenging problems and coming up with their own ideas, which they will be encouraged to brainstorm with their peers and managers. They should be willing to learn new techniques and be open to solving problems with an interdisciplinary approach. They must have excellent coding skills and should demonstrate a willingness to write modular, reusable, and functional code.
What you’ll do
- Collaborate with data scientists working with Python, LLMs, NLP, and Generative AI to design, fine-tune, and deploy intelligent agents and chains-based applications.
- Develop and maintain front-end interfaces for AI and data science applications using React.js / Angular / Nextjs and/or Streamlit/ DASH, enhancing user interaction with complex machine learning and NLP-driven systems.
- Build and integrate Python-based machine learning models with backend systems via RESTful APIs using frameworks like FastAPI / Flask or Django.
- Translate complex business problems into scalable technical solutions, integrating AI capabilities with robust backend and frontend systems.
- Assist in the design and implementation of scalable data pipelines and ETL workflows using DBT, PySpark, and SQL, supporting both analytics and generative AI solutions.
- Leverage containerization tools like Docker and utilize Git for version control, ensuring code modularity, maintainability, and collaborative development.
- Deploy ML-powered and data-driven applications on cloud platforms such as AWS or Azure, optimizing for performance, scalability, and cost-efficiency.
- Contribute to internal AI/ML Ops platforms and tools, streamlining model deployment, monitoring, and lifecycle management.
- Create dashboards, visualizations, and presentations using tools like Tableau/ PowerBI, Plotly, and Seaborn to drive business insights.
- Proficient with Excel, and PowerPoint by showing proficiency in business communication through stakeholder interactions.
About you
- A Master’s degree or higher in Computer Science, Data Science, Engineering, or related fields OR Bachelor's candidates with relevant industry experience will also be considered.
- Proven experience (2 years for Master’s; 3+ years for Bachelor’s) in AI/ML, software development, and data engineering.
- Solid understanding of LLMs, NLP, Generative AI, chains, agents, and model fine-tuning methodologies.
- Proficiency in Python, with experience using libraries such as Pandas, Numpy, Plotly, and Seaborn for data manipulation and visualization.
- Experience working with modern Python frameworks such as FastAPI for backend API development.
- Frontend development skills using HTML, CSS, JavaScript/TypeScript, and modern frameworks like React.js; Streamlit knowledge is a plus.
- Strong grasp of data engineering concepts – including ETL pipelines, batch processing using DBT and PySpark, and working with relational databases like PostgreSQL, Snowflake etc.
- Good working knowledge of cloud infrastructure (AWS and/or Azure) and deployment best practices.
- Familiarity with MLOps/AI Ops tools and workflows including CI/CD pipelines, monitoring, and container orchestration (with Docker and Kubernetes).
- Good-to-have: Experience in BI tools such as Tableau or PowerBI,
- Good-to-have: Prior exposure to consulting projects or CP (Consumer Products) business domain.
