Senior Data Scientist Engineer

The Senior Data Scientist and Data Engineer will lead the development, implementation, and maintenance of advanced data analytics and LLM models, as well as the infrastructure required for optimal data processing. This role involves working with large datasets, developing predictive models, and ensuring the availability, reliability, and performance of data systems.

KEY RESPONSIBILITIES:

Data Analysis and Modeling:

1. Analyze large and complex datasets to extract actionable insights.
2. Develop and implement predictive models and machine learning algorithms.
3. Validate and refine models to ensure accuracy and reliability.
4. Design and implement robust ETL pipelines to process and manage large datasets.
5. Ensure data is clean, reliable, and accessible for analysis and modeling.
6. Optimize data pipelines for performance and scalability.

Collaboration and Communication:

1. Work closely with stakeholders to understand business requirements and translate them into data science and engineering solutions.
2. Communicate findings and recommendations to technical and non-technical audiences.
3. Collaborate with data scientists, analysts, and software developers to deploy models into production.

Research and Innovation:

1. Stay updated with the latest advancements in data science, AI, and big data technologies.
2. Explore new tools, techniques, and methodologies to enhance data science and engineering capabilities.
3. Lead research initiatives to prototype and pilot new data solutions.

Quality Assurance:

1. Ensure the quality and integrity of data and models through rigorous testing and validation.
2. Implement best practices for data science and engineering, including version control and documentation.
3. Conduct regular reviews and audits to ensure compliance with industry standards and regulations.

Innovation and Continuous Improvement:

1. Identify opportunities for process improvements and implement changes to enhance data infrastructure and analytics.
2. Promote a culture of continuous learning and innovation within the team.

QUALIFICATIONS & EXPERIENCE:

1. Bachelor's degree in a relevant field.
2. At least 5 years of experience in data science and data engineering or a related field.