AI Governance Consultant, Amadora Amadora

AI Governance Consultant, Amadora
Amadora
Amadora, Lisboa, Portugal

AI Governance Consultant

About the Role

Location: Portugal, Lisboa, Amadora

Country/Region: Germany

State/Province/County: Land Berlin

City: Berlin

Remote vs. Office: Hybrid (Remote/Office)

Company: Siemens Energy Unipessoal Lda.

Organization: EVP Global Functions

Business Unit: Enterprise Data & Advanced Analytics

Full / Part time: Full-time

Experience Level: Experienced Professional

A Snapshot of Your Day

Are you passionate about AI and its ethical implications? Do you want to work on challenging and impactful projects that shape the future of AI governance? If so, we have an exciting opportunity for you.

How You’ll Make an Impact

1. Conduct research and analysis on various aspects of AI governance, such as fairness, accountability, transparency, privacy, and human rights.
2. Develop and implement
   - driven solutions and tools to address AI governance challenges and opportunities.
3. Collaborate with stakeholders from different sectors and backgrounds, such as government, industry, academia, and civil society, to foster dialogue and alignment on AI governance issues and best practices.
4. Contribute to the development and dissemination of AI governance frameworks, standards, and guidelines.

To be successful in this role, you should have:

1. A master's degree or Ph
   D in computer science, statistics, engineering, or a related field, with a focus on AI or machine learning.
2. At least three years of relevant experience in data science, AI, or machine learning, preferably in an applied or
   - oriented setting.
3. A strong understanding of AI governance principles, concepts, and methods, and their applications and implications across different domains and contexts.
4. Proficiency in programming languages and tools for data analysis and AI, such as Python, R, SQL, Tensor
   Flow, Py
   Torch, etc.
5. Experience in data collection, cleaning, processing, visualization, and modeling, using various sources and types of data, such as structured, unstructured, text, image, audio, video, etc.
6. Experience in developing and evaluating AI systems and solutions, using various techniques and metrics, such as supervised, unsupervised, and reinforcement learning, classification, regression, clustering, natural language processing, computer vision, etc.