Bridging 20 years of policy expertise with cutting-edge data science
PhD in Political Science | BSc Computer Science (in progress) | Prague-based

I'm a career transitioner combining deep domain expertise in public policy with formal training in computer science and machine learning. After two decades working across multinational corporations (Mondelēz International), government (British Embassy Prague) and academia, I'm now building production-ready ML systems focused on GovTech and PolicyTech applications.

At a Glance:

• PhD Political Science + BSc Computer Science (in progress)
• 20 years: Government, Corporate, Academia
• Target: ML Engineer in GovTech (2028)
• Prague, CZ | Open to remote/EU opportunities

Current Status (October 2025):

• BSc Computer Science at University of London (Academic Direction: Goldsmiths) | Level 4, targeting First Class Honours
• Specialization in Data Science & Machine Learning (Level 6)
• Target Role: Data Scientist / ML Engineer in GovTech, PolicyTech or Social Impact AI (2028)

Most data scientists learn domain knowledge after technical training. I'm doing it in reverse by bringing 20 years of institutional knowledge, regulatory insight and stakeholder communication to data-driven problem-solving. This positions me uniquely for:

• GovTech AI applications (regulatory compliance, policy impact modeling)
• Interpretable ML systems (where stakeholder trust and explainability are critical)
• Cross-functional leadership (translating between technical teams and executive decision-makers)

End-to-end policy sentiment analysis using real-world government data

• Tech Stack: Python, Pandas, Scikit-learn, Transformers, Docker, AWS
• Focus: NLP for regulatory text classification, policy impact forecasting
• Timeline: Dec 2025 initial release

Curated portfolio of university coursework and learning progression

• Level 4: Computational Mathematics, Python fundamentals, Algorithms
• Level 5: Programming with Data, Software Engineering, Database Systems
• Level 6: Machine Learning, AI, Data Science specialization

Current Proficiency

Python       ████████████████░░  85%  |  Pandas, NumPy, Scikit-learn, Matplotlib
SQL          █████████████░░░░░  70%  |  Query optimization, database design
Git/GitHub   ████████████████░░  80%  |  Version control, collaborative workflows
Jupyter      ███████████████░░░  75%  |  Notebooks, data exploration, reporting
R            ███████████████░░░  75%  |  Statistical analysis, ggplot2

Building Expertise

Machine Learning   ██████████░░░░░░░░  50%  |  Scikit-learn, model evaluation
Deep Learning      ████████░░░░░░░░░░  40%  |  TensorFlow, PyTorch
NLP                ██████████░░░░░░░░  50%  |  Transformers, spaCy, NLTK
MLOps              ████░░░░░░░░░░░░░░  20%  |  Docker, CI/CD, AWS (2026)
Cloud Platforms    ███░░░░░░░░░░░░░░░  15%  |  AWS fundamentals (2026)

Domain Expertise

• Statistical analysis and data-driven policy research
• Regulatory impact assessment and compliance analysis
• Executive communication and stakeholder management
• Cross-cultural team leadership (CZ, UK, AU, US)

BSc Computer Science (Data Science specialization)
University of London (Academic Direction: Goldsmiths) | Oct 2025 - Apr 2028

• Target: First Class Honours (70%+ average)

PhD in Political Science
Charles University, Prague | 2013

• Methods: Statistical modeling, network analysis, computational text analysis

• Mathematics for Machine Learning Specialization at Imperial College London (2024)
• Introduction to Statistics at Stanford University (2024)
• IBM Data Science at IBM (2024)

View all certificates

Phase 1: Foundation (Oct 2025 - Apr 2026)

• Complete Level 4 Computer Science modules
• Build Public Affairs ML project
• Establish GitHub technical documentation

Phase 2: Specialization (Oct 2026 - Sep 2027)

• Complete ML/Data Science modules (Level 5-6)
• Develop production MLOps projects
• Contribute to open source AI/policy projects

Phase 3: Career Entry (Oct 2027 - Sep 2028)

• Complete BSc with Data Science specialization
• Target Data Scientist / ML Engineer roles in GovTech
• Final project: AI application in governance domain

Contribution Focus:

• Academic coursework repositories
• Production-ready ML applications
• Technical documentation

Most ML practitioners come from pure CS/Math backgrounds. I bring:

• Domain Expertise: 20 years in public affairs, government and corporate strategy
• Global Perspective: International experience across Europe, US and Australia
• Research Rigor: PhD-level statistical and computational methods
• Communication Skills: Executive-level stakeholder management
• Ethical Focus: Deep understanding of AI governance and responsible ML

I don't just build ML models. I understand the institutional context where they'll be deployed.

After 20 years helping organizations navigate complex policy landscapes, I've witnessed how data-driven insights transform decision-making. Now I'm building the technical skills to create those insights as production systems that scale.

What excites me most is the convergence of interpretability, ethics and impact in AI. The best ML systems aren't just accurate. They're explainable to stakeholders, aligned with institutional values and designed to augment human judgment.

Immediate (2025-2026):

• Study buddies for University of London CS degree
• Mentorship from ML practitioners in GovTech/PolicyTech
• Collaborative projects combining policy and data science

Future (2027-2028):

• Data Science internships in GovTech
• ML consulting opportunities
• Full-time ML Engineer/Data Scientist roles

Interested in:

• GovTech/PolicyTech applications of ML and AI
• Interpretable AI for high-stakes decision-making
• AI ethics and governance frameworks
• NLP for policy analysis and regulatory text processing
• Open source civic tech contributions

Open to:

• Collaborative projects at the intersection of data science and policy
• Freelance/consulting in GovTech AI
• Mentorship from experienced ML practitioners
• Speaking on career transitions in tech

Contact:
Email: sebekj@gmail.com
LinkedIn: jiri-sebek
Bluesky: @sebekjphd.bsky.social

This Quarter (Oct-Dec 2025):

• CM1015: Computational Mathematics
• CM1005: Introduction to Programming I
• Building: Public Affairs ML Pipeline
• Reading: "Designing Machine Learning Systems" by Chip Huyen

Next Quarter (Jan-Mar 2026):

• CM1030: How Computers Work
• CM1035: Algorithms and Data Structures I
• Planned: GovTech ML Pipeline with AWS deployment