Data Scientist 4

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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