AB InBev is hiring for a fresher entry level Junior Data Scientist role in India

Position:

• Junior Data Scientist – Predictive Forecasting

Company:

• AB InBev (GCC Services India Pvt. Ltd.)

Location:

• Bengaluru, Karnataka, India

Job type:

• Full-time

Job mode:

• Onsite

Job requisition id:

• 30084171

Years of experience:

• 0–3 years

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Company Description (in bullet format)

• AB InBev is recognized as the world’s largest brewing company.

• Its vision extends beyond business, striving to bring people together through the culture of beer.

• The company owns more than 500 beer brands, including globally known names such as Budweiser, Corona, and Stella Artois.

• AB InBev is committed to sustainable growth that benefits communities, consumers, farmers, and partners alike.

• Their mission focuses on building a company that will last for a century, investing in both people and local ecosystems.

• With a long history of uniting cultures over a beer, they continue to evolve in today’s fast-paced, connected world.

• AB InBev integrates innovation and tradition, crafting beers using quality ingredients while embracing technological advances.

• The organization values diversity, inclusion, and collaboration as foundational elements of its workforce.

• They actively support local communities, not only through products but also through long-term economic and social initiatives.

• At its core, AB InBev sees brewing beer as a cultural bridge, one that brings generations and geographies together.

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Profile Overview (in bullet format)

• The role is for a Junior Data Scientist within the Growth Analytics Centre at AB InBev in Bengaluru.

• You will work on statistical and predictive modeling focused on business performance forecasting.

• Your primary responsibility will be to design and implement time series forecasting models for internal business cycles.

• You will be expected to extract actionable insights from these forecasts and translate them into business strategies.

• The role involves close interaction with other data scientists, developers, and stakeholders from cross-functional departments.

• A strong analytical mindset and a structured approach to problem-solving are crucial for success in this position.

• You will contribute to the ongoing performance monitoring and optimization of existing models.

• As part of a larger analytics team, you’ll engage in continuous learning, solution iteration, and model refinement.

• The position is ideal for someone looking to apply machine learning in a real-world, fast-paced business environment.

• You will be part of a culture that values growth, experimentation, and innovative thinking.

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Qualifications (in bullet format)

• A Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a closely related field is required.

• Prior experience in time series modeling or predictive forecasting is an added advantage.

• Practical knowledge of statistical and machine learning techniques is expected.

• Strong skills in Python, especially in the areas of data manipulation, object-oriented programming, and working with modules and packages.

• Understanding of MLOps fundamentals including model development, version control systems, CI/CD practices, model deployment, and monitoring.

• Familiarity with machine learning libraries such as scikit-learn is important; experience with TensorFlow is a plus.

• Basic to intermediate understanding of Git including branching, pull requests, and code reviews.

• Experience using data visualization platforms like Power BI, matplotlib, plotly, or streamlit.

• Proficiency in data analysis tools such as Pandas and Excel including pivot tables, macros, and shortcut formulas.

• Strong communication skills and an ability to present findings through PowerPoint or dashboards.

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Additional Info (in bullet format)

• This role sits within the Growth Analytics Centre, one of AB InBev’s global hubs for business intelligence and analytics.

• You’ll work on large, complex datasets that are business-critical in nature and updated in real-time.

• You will be expected to convert model outputs into understandable and actionable business recommendations.

• The work will have a direct impact on strategic and operational decision-making.

• You’ll present your insights to senior stakeholders and participate in shaping business KPIs.

• Staying updated on the latest research in time series forecasting and machine learning is encouraged.

• The position offers significant scope for professional development and exposure to international analytics practices.

• You’ll collaborate in an environment that values curiosity, ownership, and agility.

• This role supports not only the analytics team but also has touchpoints with product and business operations.

• Performance improvement and continuous feedback loops are part of the company’s working culture.

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Responsibilities (in bullet format)

Model Development and Forecasting:

• Design and maintain time series forecasting models tailored for business metrics.

• Integrate advanced statistical methods and machine learning algorithms into modeling pipelines.

• Regularly update forecasting models based on changing business dynamics and seasonality.

Data Analysis and Insight Extraction:

• Analyze high-volume datasets to identify trends, outliers, and underlying patterns.

• Translate data findings into actionable insights for senior management and business leads.

• Use exploratory data analysis techniques to validate model assumptions and improve accuracy.

Stakeholder Collaboration:

• Work closely with other data scientists and developers to ensure models are scalable and deployable.

• Engage with cross-functional teams to align models with business goals.

• Help translate ambiguous business problems into structured analytical questions.

Reporting and Presentation:

• Create reports, dashboards, and presentations that communicate technical insights in a business-friendly format.

• Summarize findings using data visualization and storytelling techniques.

• Communicate model impact, performance metrics, and recommended actions effectively.

Continuous Learning and Innovation:

• Keep track of the latest developments in the field of time series forecasting and machine learning.

• Experiment with new algorithms and techniques and evaluate their applicability to current business problems.

• Identify opportunities for model automation and process streamlining.

Please click here to apply.