Data Scientist

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do?

AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics. Do You Dream Big? We Need You.

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1. PURPOSE OF ROLE

The Data Scientist will play a key role in designing and delivering data-driven solutions that enable better decision-making across the organization. This role requires strong hands-on coding skills in Python, experience with core data science libraries, and the ability to statistically validate features and models. The analyst will collaborate across teams, work efficiently with existing codebases, and apply version control and development best practices to build scalable, production-ready analytics solutions. With intermediate SQL expertise and a solid grasp of model development workflows, the role ensures robust, interpretable, and actionable outcomes from complex data.

2.KEY TASKS AND ACCOUNTABILITIES

• Develop and maintain data science models using Python, applying intermediate to advanced knowledge of syntax, data structures, and key libraries such as pandas and numpy.

• Perform feature engineering and statistical validation of features and models to ensure robustness, accuracy, and business relevance.

• Write clean, modular, and well-documented code following best development practices; optionally adopt Test-Driven Development (TDD) to enable faster iteration and feedback cycles.

• Collaborate with cross-functional teams to understand data requirements, align on analytical solutions, and translate business problems into data science problems.

• Read, understand, and extend existing codebases, adapting quickly to different coding styles and project structures.

• Leverage version control tools like Git for collaborative development, code management, and maintaining reproducibility of models.

• Write and optimize intermediate-level SQL queries to extract, transform, and analyze data from structured databases.

• Contribute to the deployment readiness of models, ensuring outputs are interpretable, reusable, and aligned with production or decision-support use cases.

• Document processes, assumptions, and outputs clearly for stakeholder transparency, reproducibility, and future reference.

• Stay up to date with industry trends, new tools, and emerging best practices in data science, analytics, and development methodologies.

3. QUALIFICATIONS

• Bachelor’s or master’s degree in computer science, Information Systems, Artificial Intelligence, Machine Learning, or a related field (B. Tech / BE / Masters in CS/IS/AI/ML).

Work Experience:

• Minimum of 3 years of hands-on experience in a data science or analytics role, with a proven track record of building and deploying data-driven solutions in real-world scenarios.

4. Technical Skills Required:

• Python Programming (Intermediate to Advanced): Strong grasp of syntax, data structures, and experience with libraries like pandas and numpy.

• Data Science Fundamentals: Ability to statistically validate features and models, ensuring sound analytical rigor.

• SQL (Intermediate): Proficiency in writing queries to extract, manipulate, and analyse data from relational databases.

• Version Control (GIT): Familiarity with collaborative development using Git for code versioning and management.

• Code Adaptability: Comfortable working with and modifying existing codebases written by others.

5. Good to have skills:

• Object-Oriented Programming (OOPs) in Python: Understanding and applying OOP concepts where appropriate.

• Test-Driven Development (TDD): Awareness of TDD practices for faster iteration and improved code quality.

• Model Deployment Lifecycle Knowledge: Familiarity with reproducibility, tracking, and maintaining deployed models (though not explicitly required, it’s a plus if known).