Sr. Data Scientist

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.

Job Description

Job Title : Senior Data Scientist

Location : Bangalore

Reporting to : Senior

Manager

1. Purpose of the role

This role sits at the intersection of data science and revenue growth strategy, focused on developing advanced analytical solutions to optimize pricing, trade promotions, and product mix. The candidate will lead the end-to-end design, deployment, and automation of machine learning models and statistical frameworks that support commercial decision-making, predictive scenario planning, and real-time performance tracking.

By leveraging internal and external data sources—including transactional, market, and customer-level data—this role will deliver insights into price elasticity, promotional lift, channel efficiency, and category dynamics. The goal is to drive measurable improvements in gross margin, ROI on trade spend, and volume growth through data-informed strategies.

2. Key tasks & accountabilities

Design and implement price elasticity models using linear regression, log-log models, and hierarchical Bayesian frameworks to understand consumer response to pricing changes across channels and segments.

Build uplift models (e.g., Linear Regression, XGBoost for treatment effect) to evaluate promotional effectiveness and isolate true incremental sales vs. base volume.

Develop demand forecasting models using ARIMA, SARIMAX, and Prophet, integrating external factors such as seasonality, promotions, and competitor activity, time-series clustering and k-means segmentation to group SKUs, customers, and geographies for targeted pricing and promotion strategies.

Construct assortment optimization models using conjoint analysis, choice modeling, and market basket analysis to support category planning and shelf optimization.

Use Monte Carlo simulations and what-if scenario modeling to assess revenue impact under varying pricing, promo, and mix conditions.

Conduct hypothesis testing (t-tests, ANOVA, chi-square) to evaluate statistical significance of pricing and promotional changes.

Create LTV (lifetime value) and customer churn models to prioritize trade investment decisions and drive customer retention strategies.

Integrate Nielsen, IRI, and internal POS data to build unified datasets for modeling and advanced analytics in SQL, Python (pandas, statsmodels, scikit-learn), and Azure Databricks environments.

Automate reporting processes and real-time dashboards for price pack architecture (PPA), promotion performance tracking, and margin simulation using advanced Excel and Python.

Lead post-event analytics using pre / post experimental designs, including difference-in-differences (DiD) methods to evaluate business interventions.

Collaborate with Revenue Management, Finance, and Sales leaders to convert insights into pricing corridors, discount policies, and promotional guardrails.

Translate complex statistical outputs into clear, executive-ready insights with actionable recommendations for business impact.

Continuously refine model performance through feature engineering, model validation, and hyperparameter tuning to ensure accuracy and scalability.

Provide mentorship to junior analysts, enhancing their skills in modeling, statistics, and commercial storytelling.

Maintain documentation of model assumptions, business rules, and statistical parameters to ensure transparency and reproducibility.

Other Competencies Required

Presentation Skills : Effectively presenting findings and insights to stakeholders and senior leadership to drive informed decision-making.

Collaboration : Working closely with cross-functional teams, including marketing, sales, and product development, to implement insights-driven strategies.

Continuous Improvement : Actively seeking opportunities to enhance reporting processes and insights generation to maintain relevance and impact in a dynamic market environment.

Data Scope Management : Managing the scope of data analysis, ensuring it aligns with the business objectives and insights goals.

Act as a steadfast advisor to leadership, offering expert guidance on harnessing data to drive business outcomes and optimize customer experience initiatives. Serve as a catalyst for change by advocating for data-driven decision-making and cultivating a culture of continuous improvement rooted in insights gleaned from analysis.

Continuously evaluate and refine reporting processes to ensure the delivery of timely, relevant, and impactful insights to leadership stakeholders while fostering an environment of ownership, collaboration, and mentorship within the team.

Business Environment

Main Characteristics :

Work closely with Zone Revenue Management teams.

Work in a fast-paced environment.

Provide proactive communication to the stakeholders.

This is an offshore role and requires comfort with working in a virtual environment. GCC is referred to as the offshore location.

The role requires working in a collaborative manner with Zone / country business heads and GCC commercial teams.

Summarize insights and recommendations to be presented back to the business.

Continuously improve, automate, and optimize the process.

Geographical Scope : Europe

3. Qualifications, Experience, Skills

Level of educational attainment required :

Bachelor or Post-Graduate in the field of Business & Marketing, Engineering / Solution, or other equivalent degree or equivalent work experience.

MBA / Engg. in a relevant technical field such as Marketing / Finance.

Extensive experience solving business problems using quantitative approaches.

Comfort with extracting, manipulating, and analyzing complex, high volume, high dimensionality data from varying sources.

Previous work experience required

5-8 years of experience in the Retail / CPG domain.

Technical skills required

Data Manipulation & Analysis : Advanced proficiency in SQL, Python (Pandas, NumPy), and Excel for structured data processing.

Data Visualization : Expertise in Power BI and Tableau for building interactive dashboards and performance tracking tools.

Modeling & Analytics : Hands-on experience with regression analysis, time series forecasting, and ML models using scikit-learn or XGBoost.

Data Engineering Fundamentals : Knowledge of data pipelines, ETL processes, and integration of internal / external datasets for analytical readiness.

Proficient in Python (pandas, scikit-learn, statsmodels), SQL, and Power BI.

Skilled in regression, Bayesian modeling, uplift modeling, time-series forecasting (ARIMA, SARIMAX, Prophet), and clustering (k-means).

Strong grasp of hypothesis testing, model validation, and scenario simulation.

And above all of this, an undying love for beer!

We dream big to create future with more cheers.