Manager, Data Science (Supply Chain) - Global Capability Center

Description

• Performance improvement via cost reduction and revenue growth programmes
• Large, complex organizational restructuring and business transformation
• Rapid Results Program, which helps to quickly and effectively assess business process management risks and identify key areas impeding performance

• Develop and automate data pipelines using Python for ingestion, cleansing, and transformation of supply chain data
• Design predictive analytics models in Python (e.g., demand forecasting, inventory optimization) using libraries such as pandas, scikit-learn, stats models, and Prophet
• Implement anomaly detection models to flag irregularities in procurement costs, delivery timelines, and inventory movements.
• Develop end-to-end ETL workflows for integrating data from SAP, SQL databases, and Excel-based trackers.
• Use Python for simulation of what-if scenarios in logistics, supply planning
• Hyper Parameter tuning to enhance the performance of models
• Design A/B tests and apply causal inference techniques for business impact analysis
• Strong in statistics and probability: hypothesis testing, confidence intervals, and bootstrapping
• Work with cloud platforms (AWS, GCP, Azure) and tools such as S3, Databricks
• Create interactive visualizations and dashboards using Plotly, Seaborn, and Power BI for communicating insights

• Process know-how of supply chain planning and Logistics operations
• Build and deploy statistical and machine learning models (ARIMA, XGBoost, Prophet) for demand forecasting, supply planning and Logistics optimization
• Hands-on experience with Pyomo, PuLP, OR-Tools, and heuristics for scheduling, routing, and load optimization
• Build scenarios to analyze how varying key inputs like number of trucks, DC’s and Dealers etc. influence the performance of entire network
• Create interactive dashboards using Power BI and Python for tracking logistics KPIs and operational metrics
• Present data led cases / scenarios and model outputs to leadership for decision support and implementation readiness

• Minimum of 6–11 years of experience in data science / modelling for functions across supply chain, i.e., logistics, transportation, distribution strategy, or downstream supply chain execution roles
• Hands-on experience of minimum 3-4 years in python with focus on statistical analysis and machine learning
• Previous advisory experience from a top-tier strategy firm, niche logistics consulting firm, or Big-4 consultancy preferred
• Bachelor’s degree in engineering, logistics, supply chain, or a related field
• Master’s degree in Computer Science / Operations Research / Industrial Engineering is preferred
• Strong storyboarding skills to translate operational insights into impactful client presentations
• Excellent communication and interpersonal skills with high motivation to drive logistics transformation
• Ability to manage multiple logistics-focused engagements and meet tight deadlines
• Experience in KPI tracking and performance dashboards
• Strong analytical, quantitative, and root-cause problem-solving skills

Inclusive Diversity

A&M’s entrepreneurial culture celebrates independent thinkers and doers who can positively impact our clients and shape our industry. The collaborative environment and engaging work—guided by A&M’s core values of Integrity, Quality, Objectivity, Fun, Personal Reward, and Inclusive Diversity—are the main reasons our people love working at A&M. Inclusive Diversity means we embrace diversity, and we foster inclusiveness, encouraging everyone to bring their whole self to work each day. It runs through how we recruit, develop employees, conduct business, support clients, and partner with vendors. It is the A&M way.

Equal Opportunity Employer

It is Alvarez & Marsal’s practice to provide and promote equal opportunity in employment, compensation, and other terms and conditions of employment without discrimination because of race, color, creed, religion, national origin, ancestry, citizenship status, sex or gender, gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, family medical history, genetic information or other protected medical condition, political affiliation, or any other characteristic protected by and in accordance with applicable laws. .

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