Responsibilities
- Lead the identification, creation, and implementation of machine learning models such as regression, classification, clustering, and time-series forecasting to address sector-specific problems.
- Collaborate with key stakeholders to define high-impact opportunities and convert ambiguous business needs into actionable technical plans.
- Build and refine data pipelines and feature repositories, maintaining data accuracy and consistency across various systems including SQL, NoSQL, and data lakes.
- Apply statistical methods to design experiments, including A/B tests and causal analysis, to measure the effectiveness of business initiatives.
- Collaborate with machine learning engineers to adopt MLOps standards like model version control, automated testing, and continuous integration and deployment.
- Develop scalable and modular analytical systems that can be reused across multiple client projects or internal platforms.
- Present intricate technical results to non-technical leadership using clear narratives and visual data representations.
- Serve as a technical mentor, conducting code evaluations, offering architectural direction, and supporting junior team members.
- Remain current with advancements in artificial intelligence, assessing and incorporating new methods in deep learning, reinforcement learning, or optimization into existing toolsets.