Assistant Manager, Global Data Science

Brief introduction - Role Summary/Purpose

• The Global Data Science team at Colgate-Palmolive builds and deploys analytics solutions to drive business growth, focusing on areas like Price Elasticity, Trade Promo Efficiency, and Marketing Effectiveness.

• To meet increasing demand, new geographically based "Advanced Analytics Pods" are being created, with this role leading the "Europe and Africa Eurasia Pod" and managing six data scientists.

• This position involves close collaboration with the capability center, Division Analytics and business teams to understand needs, prioritize projects, and deliver relevant analytics solutions.

• The role requires strong expertise in statistical data analytics, machine learning/AI, and CPG analytics to develop predictive models, manage large datasets, and translate insights into business impact.

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.

• Client-Facing & Communication Skills: Proven experience in a client-facing role, supporting multi-functional teams, with exceptional communication, collaboration, presentation, and storytelling skills to convey insights to business stakeholders.

• Project Leadership & Execution: Ability to independently plan and execute deliveries, lead project teams for predictive modeling and ad-hoc data needs, and build processes for data transformation and management.

Preferred Qualifications

• Proficient in building web applications using frameworks like Pydash, Plotly, R Shiny, Streamlit, Django, Dash, Python Flask, React.js, or Angular JS.

• Experienced with advanced AI and machine learning concepts including Deep Learning, Reinforcement Learning, Image Recognition, GenAI, and understanding the real-world application of various ML techniques.

• Strong knowledge of cloud environments and MLOps tools, including Databricks, Docker, Azure Data Factory, machine learning APIs (Azure, AWS, Vertex AI), Snowflake, and Google Cloud components (Cloud Build, Cloud Run, Kubernetes).

• Familiar with data visualization tools (Domo, Looker, Streamlit), working with third-party data (syndicated market data, Point of Sales), and has experience in Data Science roles within the CPG industry, ideally in relevant geographies.