Job responsibilities:
- Lead model data management, focusing on data quality, validation, enrichment, analytics, reporting, and exception monitoring.
- Develop AI/ML model accuracy threshold recommendations and provide performance reporting to leadership.
- Implement strategies aligning data management with operational objectives.
- Innovate and modernize data processes with cutting-edge technologies.
- Lead operational change initiatives for smooth transitions and new system implementations.
- Foster a culture of continuous improvement using data insights.
- Communicate strategies and foster collaboration with cross-functional teams and senior leadership.
- Implement robust oversight and governance models for vendors and service providers.
- Deliver presentations to convey strategies and results to stakeholders.
- Oversee the planning and execution of the roadmap with cross-functional teams.
Required Qualifications, Capabilities, and Skills:
- 7+ years in data science, data management, and operational leadership.
- Proven track record in data-driven initiatives and transformational projects.
- Strong problem-solving, decision-making, and strategic thinking abilities.
- Exceptional leadership and communication skills.
- Experience in operational controls and improving frameworks.
- Advanced proficiency in Microsoft applications and developing presentations.
- Proficiency in AI, automation tools, and emerging technologies.
- Practical knowledge of JIRA for building roadmaps.
- Knowledge of the ML workflow, spanning from annotation, model training, model serving, scoring, pre/post processing, productionization and feedback capture.
Preferred qualifications, capabilities, and skills:
- Holds a Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Business, or a related field.
- Has experience in the tech industry or with digital products.
- Familiar with machine learning and AI applications in product development.
- Proven experience in business analysis and driving operational change/system development, with the ability to identify critical requirements and potential gaps.
- Possesses excellent analytical, quantitative, and problem-solving skills, along with demonstrated research ability.
- Strong communication skills, capable of presenting findings to a non-technical audience.
- Knowledgeable about the data ecosystem, including data ingestion, engineering, quality, orchestration, infrastructure, usage, privacy, and governance, as well as machine learning/data science theory, techniques, and tools