Assistant Manager, Global Data Science

Responsibilities

• Leads the scoping and delivery of Pricing, Promotion, and Marketing Effectiveness analytics, including conceptualizing and building predictive models, machine learning algorithms, and optimization solutions.
• Plays a key role in the governance and continuous improvement of internal analytics solutions, collaborating with other Advanced Analytics Pods.
• Translates complex analytical results and insights into easily understandable and actionable recommendations for business teams, effectively bridging the gap between business needs and data science.
• Develops and manages visualization deliverables and web applications to enable self-service analytics and diagnostics for various functions.
• Writes SQL and Python code to build and deploy scalable enterprise-level data science solutions (e.g., Marketing Mix Modeling, Revenue Growth Management) on cloud platforms using tools like Airflow, Docker, and Kubernetes.
• Leads, mentors, and coaches a team of data scientists, ensuring the quality of recommendations and solutions deployed globally, and drives new value by integrating internal and external data.
• Collaborates effectively with cross-functional teams (GIT, Data Architecture, Analytics Engineering) and business partners across geographies and time zones, assisting with change management and presenting insights to stakeholders.
• Required Qualifications
• Educational Background & Experience: Requires a Bachelor's degree (Master's/MBA preferred) in a quantitative/technical field (Data Science, Engineering, Statistics, Economics, etc.) with 7+ years in data science and analytics, including 2+ years managing/setting up a data science team.
• Advanced Technical Expertise: Strong hands-on experience developing and deploying statistical models and machine learning algorithms (Regression, Random Forest, XGBOOST, Clustering, etc.) in production, with expert coding skills in Python, SQL, PySpark, and R.
• MLOps & Cloud Proficiency: Demonstrable experience with MLOps practices (GitHub, Airflow, Docker, Kubernetes, Databricks, Dataiku) and cloud platforms like Google Cloud and Snowflake, managing high-volume, varied data sources.
• Domain Specialization & Business Acumen: Expert knowledge in Marketing Mix Modeling, Revenue Growth Management, Optimization, Forecasting, and Marketing Analytics, preferably in CPG or consulting, with the ability to think strategically in complex business contexts.