Role and Responsibilities
Data Analysis & Business Strategy
- Work with stakeholders across the organization to identify opportunities for leveraging data to drive business decisions.
- Mine and analyze data from company databases to support optimization and improve business strategies.
- Assess the effectiveness and accuracy of data sources and data collection methods.
- Develop and apply custom data models and algorithms to large data sets for actionable insights.
- Use predictive modeling to influence and optimize key business outcomes.
Model Development & Implementation
- Develop, operationalize, and deploy models/algorithms as structured programs or operational processes.
- Coordinate with cross-functional teams to implement models and track performance outcomes.
- Develop tools and processes to monitor model performance and ensure ongoing data accuracy.
Business Impact
- Provide actionable recommendations to business stakeholders based on model outputs.
- Drive implementation of these recommendations through changes in processes, technology, or data management practices.
- Focus areas: Primary PSCM/VMI business; Secondary ICS KPIs.
- Deliver measurable improvements in time or cost efficiencies post-implementation.
- Track and report business user satisfaction scores for delivered models and tools.
Quality, Safety, and Continuous Improvement
- Demonstrate commitment to Quality, Health, Safety, and Environment (QHSE) standards.
- Apply internal and client-specific safety and quality management systems.
- Promote a culture of continuous improvement, setting an example in achieving and exceeding company goals.
Qualifications and Education Requirements
- Graduate (BSc/BTech) in Applied Sciences, with coursework in statistics (minimum 2 years).
- Minimum 8 years of relevant industry experience.
- Relevant internship (at least 2 months) or certifications in data science/machine learning preferred.
Preferred Skills
- Strong problem-solving skills with a business-oriented mindset.
- Proficient in the following:
- R or Python for data cleaning, statistical analysis, and modeling.
- SQL, VBA, and DAX (Power BI) for querying and data manipulation.
- Understanding of data architecture principles.
- Practical knowledge of machine learning techniques (e.g., clustering, decision trees, neural networks).
- Advanced understanding of statistical methods (regression, distributions, statistical tests).
- Excellent communication skills (written and verbal) for working across teams.
- Passion for learning and adopting new technologies.
Soft Skills and Key Competencies
- Strong analytical mindset with attention to detail.
- Skilled in negotiation and influencing stakeholders.
- Flexible and adaptable with a never give up attitude.
- Ability to work independently and as part of cross-functional teams.
