**Description**:
**Key Responsibilities**
- **Data Modeling and Pipeline Management**
- Build and fine-tune classification models in the areas of spend management, contract management, and operational effectiveness.
- Develop and maintain data pipelines and architectures for efficient data processing and analysis.
- Stay informed of new techniques for predictive/prescriptive modeling.
- **Automation Development**
- Identify and action upon opportunities to automate repetitive tasks from within the team.
- Monitor and maintain automated processes to ensure optimal performance.
- Mentor junior team members on automation skillsets.
- **Insights & Reporting**
- Perform exploratory data analyses, which may include machine learning, to uncover trends, patterns, and insights from large and complex datasets.
- Develop, test, and implement data-driven solutions to support business objectives.
- Oversee the quality and effectiveness of team dashboards and reports.
- **Data Management & Quality**
- Design and implement data collection and data quality processes.
- Ensure data integrity and consistency across multiple sources and systems.
- Develop and maintain documentation for data processes and analysis specifications.
- **Collaboration & Communication**
- Work closely with data engineers and business analysts to integrate data insights into production systems.
- Communicate findings and insights to non-technical stakeholders through reports and presentations.
**Qualifications**:
- ** Education**: Master's degree in Data Science, Analytics, Statistics, Computer Science, Mathematics, Engineering, or a related field.
- **Experience**: 4+ years of professional experience in data analytics, with a proven track record of building and tuning classification models.
- **Language**: Proficiency in English is required.
**Technical Skills**
- Strong proficiency in Python or R programming.
- Experience utilizing statistical methods and machine learning techniques.
- Expertise in building and deploying predictive models.
- Proficiency in Microsoft Azure, including Azure Data Factory and Databricks, or a demonstrated ability to learn new tools and platforms
- Understanding of data visualization tools, with a preference for Power BI.
- Experience with SQL for data manipulation and querying.
- **Data Management**
- Experience with data manipulation and cleaning tools (e.g., tidyverse, Pandas, Numpy).
- Familiarity with data warehousing solutions (e.g., SQL Server, Amazon Redshift, Google BigQuery).
**Soft Skills**
- Strong problem-solving skills and attention to detail.
- Excellent communication skills, with the ability to convey technical concepts to a non-technical audience.
- Ability to work independently and as part of a team in a fast-paced environment.
**Preferred Qualifications**
- Certification in Data Analysis or related fields.
- Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).
- Knowledge of Agile methodologies and version control systems (e.g., Git).
- **Procurement Background**: Experience in procurement is an added advantage.