Mercury10 days ago
Senior Manager - Управление данными и ИИ
Salary not specified
MARKET
15,900 ₽median for this role
Data Scientist · 115 jobs with disclosed pay
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San Francisco
Responsibilities
- 01Develop and implement Mercury’s enterprise Data and AI Governance frameworks, policies, standards, and operating model
- 02Establish clear accountability for data ownership, stewardship, quality, lineage, classification, access, retention, and appropriate use
- 03Create a risk-based governance process for AI use cases across their lifecycle, including intake, assessment, approval, implementation, monitoring, and retirement
- 04Develop responsible-AI principles and standards addressing transparency, explainability, fairness, privacy, security, human oversight, reliability, and regulatory compliance
- 05Maintain an enterprise inventory of material data assets, AI use cases, and related governance decisions in coordination with relevant stakeholders
- 06Define risk-based classifications and governance requirements based on the sensitivity, complexity, materiality, and customer or regulatory impact of each use case
- 07Establish governance for internally developed, vendor-provided, and embedded AI capabilities, including generative AI
- 08Partner with various Product, Engineering, Data, and business teams to embed governance requirements into development and change-management processes
- 09Coordinate with Model Risk Management to determine when an AI use case meets the definition of a model and is subject to model-risk requirements
- 10Partner with Information Security and Technology Risk on data protection, cybersecurity, access, architecture, resilience, and technology-control considerations
- 11Partner with Legal and Compliance to identify and implement applicable regulatory, contractual, consumer-protection, and privacy requirements
- 12Develop processes for identifying, documenting, escalating, and remediating data- and AI-related risks and issues
- 13Establish metrics/reporting to provide management and Board committees with visibility into data quality, governance maturity, AI adoption, exceptions, incidents, and emerging risks
- 14Monitor regulatory developments, industry practices, and emerging risks related to data and AI, and translate them into proportionate governance expectations
- 15Support relevant Data and AI governance forums/committees and facilitate timely, well-documented decisions
- 16Eventually, build and lead a high-performing Data & AI Governance team as the program matures
- 17Promote a culture in which data is treated as an enterprise asset and AI is used responsibly, transparently, and in alignment with Mercury’s risk appetite
Requirements
- 0110+ years of relevant experience in data governance, AI governance, technology risk, information governance, model risk, privacy, compliance, or a related discipline
- 02Demonstrated experience building or materially enhancing a data governance, AI governance, or responsible-AI program
- 03Strong understanding of data ownership, stewardship, quality, lineage, metadata, classification, access, retention, and lifecycle management
- 04Working knowledge of AI and machine-learning concepts, including generative AI, large language models, training and inference data, explainability, bias, performance monitoring, and human oversight
- 05Experience developing practical, risk-based policies and governance processes that can operate effectively in a fast-moving technology environment
- 06Ability to distinguish among data governance, AI governance, model risk, information security, privacy, and compliance responsibilities while coordinating effectively across those functions
- 07Strong judgment and the ability to balance innovation, customer outcomes, regulatory expectations, and risk management
- 08Demonstrated ability to influence senior executives, technical teams, and business leaders without relying solely on formal authority
- 09Excellent written and verbal communication skills, including the ability to explain complex technical and risk concepts to executive and Board audiences
- 10Experience leading teams and managing cross-functional programs with multiple stakeholders
- 11Strong 1LOD/2LOD judgment with an understanding of how enterprise Risk should govern, challenge, and partner with Engineering without taking ownership of 1LOD risks
- 12Pragmatic judgment: Translates principles into workable processes and focuses on material risks over theoretical ones
- 13Technical curiosity: Understands technical complexity while staying focused on business and customer outcomes
- 14Decisive and collaborative: Makes sound decisions amid ambiguity, moves quickly, and challenges constructively across teams