Lovet Ndialle
Bethesda,MD
Summary
Hardworking A seasoned certified AI Governance practitioner(AIGP), a certified Information Privacy Professional (CIPP/US) with over a decade of experience driving innovation and delivering substantial business outcomes in AI and Data sciences. I have excelled as a scrum master, project manager, and now as a responsible AI Governance lead. I have been championing responsible AI practices by identifying, and mitigating AI-related risks, reducing biases, and ensuring data privacy as a passionate advocate for safe, trustworthy, and responsible development, procurement, and deployment.
I've consistently implemented -data-driven strategies aligning with organizational goals while promoting transparency and fairness in AI applications. My blend of technical expertise- gained from managing generative AI teams- and leadership acumen has enabled me to effectively translate complex responsible AI concepts to diverse stakeholders.
As an RAI Governance leader, I spearhead initiatives to ensure ethical AI practices across organizations. I have delivered presentations on ethical AI, implemented guardrails, and empowered teams to leverage data science and ML/AI learning to solve critical business challenges, thereby driving competitive advantages.
My strategic vision and hands-on experience in software development and AI uniquely position me to guide organizations through AI adoption, ensuring both technological advancement and ethical responsibility.
Overview
11
11
years of professional experience
1
1
Certification
Work History
Responsible AI Governance Lead
Franklin Templeton
10.2022 - Current
- Ensured regulatory compliance, proactively identifying areas of potential risk and taking corrective actions.
- Championed corporate social responsibility initiatives, aligning company values with community expectations.
- Implemented business continuity plans, safeguarding the organization''s operations against unforeseen disruptions.
- Established strong relationships with stakeholders, promoting collaboration and building consensus on key decisions.
- Built cross-functional relationships to support organizational change.
- Conducted comprehensive audits on governance processes, ensuring adherence to established standards and best practices.
- Defined performance indicators and quality metrics to maintain compliance with governing policies, standards and adoption requirements.
- Monitored emerging trends in corporate governance, adapting strategies as needed to maintain a competitive edge.
- Defined governance roles and responsibilities to establish clear accountability for stewardship of principal information assets.
- Facilitated productive board meetings, establishing clear agendas and fostering open dialogue among members.
- Developed risk management strategies for improved organizational stability and security.
- Enhanced governance frameworks by implementing robust policies and procedures.
- Assisted in developing and maintaining department policies and procedures to support organization and industry best practices.
- Leveraged data and analytics to make informed decisions and drive business improvements.
- Promoted sustainability initiatives within the organization through effective stewardship of resources.
Generative AI/ML Agile Delivery Lead
Capital One
04.2020 - 10.2022
- I managed a project, to create trustworthy, reliable, and human-in-the-loop AI systems using machine learning to create real-time, intelligent, automated customer experiences such as informing customers about unusual charges and answering their questions in real-time. I worked with Generative AI/ML Engineers, and AI Researchers, with skills in machine learning platforms, public cloud infrastructure, LLMs, FMs, and programming languages like Python, Go, Scala, and Java.
- Key Transferable Skills for Responsible AI Governance Role
- Enforced Agile methodology to boost team productivity and ensure effective collaboration between researchers and Ml/Data engineers establish and enforce AI safety protocols.
- Proactively identify and remove obstacles to ensure smooth team progression and project continuity to create and implement AI systems, maintaining a focus on trust and reliability standards.
- - Facilitated cross-team dependency meetings maintaining clear and consistent communication with stakeholders, and the team in defining the business problems, requirement gathering, ensuring alignment, and managing expectations effectively.
- Advocated for the adoption of best practices in coding, monitoring, and security to ensure robust and reliable AI solutions.
- Establish a comprehensive stakeholder engagement and communication plan to keep all relevant parties informed about project milestones, progress, achievements, expected impacts, challenges, and changes associated with the AI implementation. Ensure regular updates and open channels for feedback to address concerns, foster collaboration and ensure strategic alignment
- Facilitated daily standup, sprint planning, sprint retrospectives, and sprint review sessions to meet project objectives and timelines.
- facilitated risk identification, categorization, analysis, monitoring, documentation, and using plan a risk response plan using different techniques like the Severity/probability harm matrix or ROAM matrix (Resolve, Own, Accept, and Mitigate) and then communicate and maintain risk registry as the risk evolve.
- Educate my team on the need to build trustworthy AI Systems by ensuring the development of AI systems with built-in human-in-the-loop oversight mechanisms to ensure responsible use and continuous human vetting, aligning with best practices in AI governance and accountability.
- Successfully led the integration of the Software Development Lifecycle (SDLC), Scaled Agile principles, and AI Development Lifecycle in the development of an AI system, ensuring timely delivery and continuous improvement through iterative feedback and cross-functional team collaboration.
AI/ML/Data Project Manager|Sr Scrum Master
United Healthcare Group
06.2019 - 04.2020
- I was the project manager of the Personalized Cancer Treatment Predictive Modeling project, to enhance personalized treatment plans for cancer patients, improve clinical decision-making, and optimize resource allocation in healthcare settings. I worked with data scientists, machine learning engineers, software engineers, and data engineers, utilizing technologies such as Pandas, SQL, Amazon Redshift for data processing and feature engineering, Jupyter Notebooks for model development, Git for version control, and Docker and AWS for deployment
- Transferable Skills for Responsible AI Governance Role
- I managed EMRs with strict HIPAA adherence, ensuring data integrity and privacy, resulting in 100% compliance during audits and reinforcing stakeholder trust.
- Implemented GDPR protocols to anticipate and mitigate risks associated with data handling. My strategic approach ensured that data impact access assessments were routinely conducted, leading to a transparent, auditable process that safeguarded user privacy and upheld regulatory standards.
- led the team in conducting comprehensive Data Protection Impact Assessments (DPIAs) to evaluate and manage risks related to data privacy. My assessments provided actionable insights that fortified data security measures, thereby ensuring informed stakeholder decisions and consistent adherence to compliance requirements.
- Ensured the team employed advanced privacy-enhancing techniques (PETs) such as anonymization, encryption, and pseudonymization to safeguard sensitive data and maintain user trust.
AI/ML/Data ProjectManager|Sr Scrum Master
M&T Bank
06.2019 - 04.2020
- We developed a lending tool that leveraged machine learning to assess the creditworthiness of loan applicants. The primary technologies used included Python for data analysis and model building, TensorFlow for machine learning, and AWS for deployment. The team comprised data engineers who handled data preprocessing and infrastructure, machine learning engineers who developed and trained the predictive models, and software engineers who integrated the models into a user-friendly application. The goal was to create an automated, accurate, and scalable system to streamline loan approval processes and reduce default rates:
- Key Transferable Skills for Responsible AI Governance Role
- Facilitated collaboration of a diverse team of AI researchers, data scientists, software engineers, ethical AI specialists, and senior stakeholders by running requirement gathering sessions, Daily stand, sprint planning sprint reviews, and sprint retrospectives.
- Led and managed multiple AI oversight initiatives from inception to deployment, ensuring alignment with ethical standards and organizational goals.
- Ensured qualitative research study on AI red teaming to identify vulnerabilities, command ensured safer AI development practices.
- Collaborated in developing and deploying a novel algorithm audit tool, empowering users to assess the fairness and transparency of AI models.
- Led organizational responses to national inquiries on AI governance, influencing policy-making and advocating for ethical standards in AI development.
- ensured my team Utilized tools and technologies such as Python, R, Git, and custom-built auditing tools for data analysis, model development, and algorithm testing
Data Project Manager|Scrum Master
DHI Group INC
02.2014 - 03.2016
- I was the Scrum Master/Project Manager for the Digital Engagement project, which integrated client data from external sources into the company's IDH system and then to downstream systems like CRM and SIMs. I also led the Client Data Analytics (CDA) Data Mastering project, centralizing sales data to reduce costs, speed up data availability, simplify management, and ensure consistency across applications like GSAM. These projects enhanced insights and client interactions, provided faster data access, and delivered significant cost savings. Our team of Data Engineers and Analysts utilized AWS, Snowflake, SQL, Python, Power BI, Business Objects, and Azure.
- Key Transferable Skills for Responsible AI Governance Role
- Established rigorous data quality assurance protocols, reducing data-related errors by 40% and ensuring compliance with AI governance frameworks, thereby safeguarding ethical and responsible data usage.
- Spearheaded the integration of multi-source data into a unified platform, improving data governance processes and reducing inconsistencies by 30%, while leading a team of 15 data professionals to enhance data insights using tools such as AWS, Snowflake, SQL, Python, and Power BI.
- Directed a multidisciplinary team of 10+ Data Engineers and Analysts, fostering cross-functional collaboration that accelerated project delivery times by 20%, and consistently met project milestones within budget and scope through strong leadership and clear communication.
Education
Bachelor of Science - Computer And Information Sciences
University Of Buea
Buea , Cameroon07.2013
Skills
- Proficient in various machine learning models such as Supervised Learning (eg, house price prediction), Unsupervised Learning(eg fraud detection), Semi-Supervised Learning, Reinforcement Learning (eg, training robots for navigation tasks, self-driver cars), and Deep Learning (eg, Natural Language Processing) to enable me to guide organizations to select the right model for their best use case and the inherent risk
- Expertise in diverse AI governance frameworks and risk classification methodologies, including OECD guidelines, EU AI Act risk categorizations, ISO 31000:2018 standards, the MIT AI risk repository, NIST AI Framework, probability and severity harms matrix, fairness matrix, confusion matrix, and risk mitigation hierarchy, to accurately identify, classify, and manage risks, and develop optimal risk treatment plans for AI systems
- Deep understanding of various AI systems (Artificial Narrow Intelligence, Artificial General Intelligence, Artificial Super Intelligence, Artificial Broad Intelligence, General Purpose AI, General Purpose AI with systems risks, Expert Systems, Fuzzy Rules, and Robotic Process Automation) and their respective use cases and risks, enabling me to determine the most suitable AI implementation for an organization
- Expertise in synthesizing and applying various Responsible AI frameworks, including Singapore's Flex AI Model Governance Framework, Trustworthy AI, UNESCO's AI Ethics Recommendations, Asilomar AI Principles, OECD AI Principles, The White House AI Bill of Rights, High-Level Expert Group on AI guidelines, and CNIL's AI Action Plan, to craft comprehensive, ethical, and transparent AI governance policies for organizations
- Expert in identifying and managing AI-related harms and risks at individual(biases, privacy concerns), group(mass surveillance), societal(Deep Fake, echo chambers, CO2 Emission ), and organizational levels(reputational, regulatory ) , as AI security issues such as hallucination, data leakage, erosion of individual freedom, operational risks, and hardware costs and engaging a proactive approach to developing comprehensive risk management strategies
- Advocate for the adoption and integration of open-source AI frameworks, including TensorFlow, PyTorch, and OpenAI's GPT models, to enhance transparency, foster collaboration, and accelerate innovation in AI research and development
- Understanding of AI development lifecycle ( planning- considering business use case, Design phase ( implementing data strategy - Data gathering, wrangling, cleansing and labeling and ensuring data privacy, Development phase building model and using feature engineering and model training implementation phase- readiness assessment and deploying and continuous monitoring
- Led cross-functional collaboration with legal teams, data scientists, and senior stakeholders to develop actionable strategies for addressing AI-related issues
- Developed and led an AI ethics community of practice, participated in industry conferences and workshops such as IBM's AI seminars, and actively engaged with professional groups including Responsible AI, IAPP, and One Trust AI Continuously expanded knowledge in AI governance and ethics, and created educational presentation materials to simplify complex topics for non-technical leaders in the organization
- Proficient in interpreting and applying data protection laws and security standards, including the General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA), to consumer-facing AI systems and automated decision-making processes Skilled in ensuring the implementation of privacy-preserving techniques like de-identification, data minimization, federated learning, and differential privacy to ensure organizational compliance and mitigate the risk of regulatory fines associated with violation of personal data and privacy
- Expert in current and emerging AI legislation; collaborate with legal experts to ensure accurate interpretation and application, mitigating potential legal and regulatory issues, including compliance with the EU AI Act, product safety laws, IP laws, and liability derivatives( fault and strict liability regimes)
- Proficient in AI resource allocation, prioritizing high-risk systems, and implementing rigorous monitoring practices In-depth knowledge of auditing and accountability frameworks (ISACA's COBIT 2019, GAO AI Framework), adept at navigating AI and data licensing challenges (IP rights), and formulating policies for third-party risk oversight Ensures proper deactivation of malfunctioning systems, develops challenger models, and conducts thorough bug bashing and red teaming exercises
- Demonstrated expertise in establishing comprehensive AI governance strategies by effectively identifying and engaging stakeholders to secure buy-in, defining and clarifying roles and responsibilities, and facilitating personnel understanding of their respective duties Proficient in maintaining a centralized inventory for all AI systems, leveraging external frameworks to mitigate risks, and concentrating on addressing key risk areas Skilled in contrasting and consolidating existing assessments to ensure cohesive and robust AI governance
- Expertise in establishing comprehensive documentation frameworks, including the development of AI terminology glossaries to standardize communication across the organization Proficient in documenting incidents, creating detailed model cards, and drafting instructional guides to ensure responsible and intended use of AI systems Demonstrated ability to understand and implement compliance protocols, with a focus on the specific requirements of high-risk AI systems for deployers, providers, and users, thereby ensuring adherence to regulatory standards and organizational policies
- Expertise in enforcing Data Privacy Impact Assessments (DPIA), conformity assessments, and related regulatory compliance is essential for identifying, evaluating, and mitigating data processing risks, ensuring adherence to legal standards, protecting personal information, avoiding legal penalties, and fostering organizational trust and sustainability
Certification
- AIGP - Certified Artificial Intelligence Practitioner
- CIPP/US - Certified Information Security Professional
- PMP - Certified Project Manager Professional
- SSM - Certified SAFe Scrum Master
- SA - Certified SAFe Agilist
- RTE - Certified SAFe Release Train Engineer
- SPC - Certified SAFe Program Consultant
- CAC - Certified Agile Coach
Timeline
Responsible AI Governance Lead
Franklin Templeton
10.2022 - Current
Generative AI/ML Agile Delivery Lead
Capital One
04.2020 - 10.2022
AI/ML/Data Project Manager|Sr Scrum Master
United Healthcare Group
06.2019 - 04.2020
AI/ML/Data ProjectManager|Sr Scrum Master
M&T Bank
06.2019 - 04.2020
Data Project Manager|Scrum Master
DHI Group INC
02.2014 - 03.2016
Bachelor of Science - Computer And Information Sciences
University Of Buea
- AIGP - Certified Artificial Intelligence Practitioner
- CIPP/US - Certified Information Security Professional
- PMP - Certified Project Manager Professional
- SSM - Certified SAFe Scrum Master
- SA - Certified SAFe Agilist
- RTE - Certified SAFe Release Train Engineer
- SPC - Certified SAFe Program Consultant
- CAC - Certified Agile Coach
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