Data Engineer at ConcreteRose Workforce Solutions

A black rose is the ideal candidate. A black rose for this role is a detail-oriented backend engineer who have strong analytical skills, experience with data manipulation and visualization, and a keen interest in AI technologies. They have experience of forming and leading a team of data scientists and machine learning engineers

Must-Haves (Non-negotiables): 

1. Experience in data analysis, with a focus on AI and machine learning applications.
2. Proven track record of working with large datasets and conducting complex data analyses.
3. Experience with data visualization tools and Python programming languages

ROLE DESCRIPTION

1. Data Collection and Preparation from various sources for analysis and modeling.
2. Exploratory data analysis to uncover insights and patterns.
3. Building dashboards and reports to communicate findings to stakeholders.
4. Using statistical and machine learning techniques to analyze data and build predictive models
5. Participating in client meetings to understand their data-related needs and provide analytical support.
6. Developing and present data-driven insights and recommendations to clients.
7. Contributing to the development and refinement of data analysis processes and best practices.
8. Identifying opportunities for automation and optimization of data-related tasks.

QUALIFICATIONS:  

1. Experience in data analysis, with a focus on AI and machine learning applications.
2. Proven track record of working with large datasets and conducting complex data analyses.
3. Experience with data visualization tools (e.g., Tableau, Power BI) and programming languages (e.g., Python, R).
4. Experience in leading or managing a team is a plus.
5. Strong proficiency in SQL for data extraction and manipulation.
6. Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, scikit-learn).
7. Experience with data preprocessing, feature engineering, and model evaluation techniques.
8. Experience in deploying machine learning models in a production environment.
9. Knowledge of cloud-based AI services (e.g., AWS AI, Google Cloud AI, Azure AI).
10. General understanding of infrastructure as code and CI/CD pipelines and the willingness to learn more

Method of Application