Senior Manager - Data Analytics

Role and Responsibilities:

• Collaborate with stakeholders across the organization to identify opportunities to leverage company data for business solutions.
• Mine and analyze data from company databases to optimize and improve business strategies.
• Assess the effectiveness and accuracy of data sources and data gathering techniques.
• Develop custom data models and algorithms tailored to various data sets.
• Use predictive modeling techniques to enhance business outcomes and efficiency.
• Work independently or with cross-functional teams to operationalize models and algorithms into software, programs, or operational processes.
• Coordinate with functional teams to implement models and monitor their performance.
• Develop tools and processes to monitor model performance and data accuracy continuously.
• Provide actionable recommendations to business users based on data and model outcomes; implement changes in operational processes, technology, or data management accordingly.
• Focus primarily on PSCM (Product Supply Chain Management) and VMI (Vendor Managed Inventory) business areas, with secondary focus on ICS KPIs (Inventory Control Systems Key Performance Indicators).
• Drive business improvements pre- and post-implementation of operational programs, algorithms, models, or software solutions.
• Measure improvements through time and cost savings, as well as satisfaction scores from business users.

Qualifications and Education:

• Graduate degree (BSc/BTech) in Applied Sciences, including at least second-year courses in Statistics.
• Relevant internship experience (minimum 2 months) or certifications in preferred skills.

Preferred Skills:

• Strong problem-solving skills focused on business development and optimization.
• Proficiency in programming languages such as:
• R or Python (data cleaning, statistical analysis, modeling libraries).
• SQL, VBA, and DAX (Power BI) for data querying and reporting.
• Knowledge of data architecture design and implementation.
• Familiarity with various machine learning techniques such as clustering, decision trees, artificial neural networks, and understanding their practical advantages and limitations.
• Experience with advanced statistical methods: regression, distributions, statistical tests, and their correct application.
• Excellent communication skills, both written and verbal, for effective cross-team coordination.
• Eagerness to learn and master new technologies and analytical techniques.

Additional Responsibilities:

• Demonstrate a strong personal commitment to Quality, Health, Safety, and Environment (QHSE) standards.
• Adhere to GET (Global Excellence Team) and Client Company's QHSE policies and safety management systems.
• Foster a culture of continuous improvement by leading by example to meet and exceed company goals.