Summary:
This position involves leading and delivering analytics initiatives, developing predictive models, designing ETL/data pipelines, and building executive-ready dashboards. The role requires collaboration with cross-functional teams to deploy analytics solutions and ensure strong data governance and operational rigor.
Responsibilities:
- Lead and deliver analytics initiatives by defining analytical approaches, building models, and translating insights into business actions for procurement and enterprise stakeholders.
- Develop, train, validate, and monitor predictive models using a broad set of machine learning/statistical methods to support targeted business outcomes.
- Design and implement ETL/data pipelines and integrate data sources to create safe, trusted datasets for reporting and analytics (including Snowflake and SQL-based workflows).
- Build executive-ready dashboards and decision tools (e.g., Power BI) that enable data-driven leadership decisions.
- Apply data modelling best practices (conceptual, logical, physical models) and support integration/transformation patterns to analytics environments and warehouses.
- Partner cross-functionally (Procurement, Digital/IT, Finance, operations stakeholders) to deploy analytics solutions into production and ensure adoption.
- Operate with strong data governance and operational rigor, including troubleshooting data issues, managing access/user needs, and supporting reliable analytics operations.
- Use modern engineering practices (e.g., GitLab/DevOps toolchains) to improve repeatability, scalability, and maintainability of analytics solutions.
Requirements:
- Degree in Computer Science, Business, Mathematics, Economics, Statistics, Engineering, or related field.
Required Skills:
- Strong AI/ML background across model development and validation, including methods such as time series, clustering, tree-based algorithms, generalized linear models, or neural networks.
- Strong SQL Snowflake proficiency for ETL, transformation, and analytics-ready datasets.
- Experience with cloud solutions, solution integration, IT operations, and data governance.
- Proficiency in Python programming language.
- Proficiency in Prompt Engineering.
- Experience with front-end technologies such as HTML, CSS, and JavaScript.
- Experience with back-end technologies such as Django, Flask, or Node.js.
- Solid grasp of database technologies such as MySQL, PostgreSQL, or MongoDB.
- Creation of CI/CD Pipelines for ML algorithms, training, prediction pipelines.
- Proficiency in Machine Learning Operations.
- Work with business, data scientists to ensure value realization, ROI on operationalization of models.
- Strong understanding of statistical analysis and machine learning algorithms.
- Containerization, packaging of ML models.
- Experience with data visualization tools such as Power BI.
- Solid grasp of data munging techniques, including data cleaning, transformation, and normalization to ensure data quality and integrity.
- Experience with Large Language Models (LLMs) and Natural Language Processing (NLP) technologies.
Preferred Skills:
- Experience with cloud-first and agile methodologies.
- Strong understanding of statistical analysis and machine learning algorithms.
- Solid grasp of data munging techniques, including data cleaning, transformation, and normalization to ensure data quality and integrity.
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