Optimus Capital - Head - Artificial Intelligence - Pharma

Role Overview:
The Head of AI will define and lead the organization's AI vision, strategy, and execution roadmap across all business functions - from investment analytics and market intelligence to portfolio company transformation and Pharma & Life Sciences Applications.
This is a highly strategic and hands-on leadership role requiring a blend of technical depth, business insight, and domain knowledge in pharmaceuticals or healthcare.
Key Responsibilities:
1. Strategic Leadership:
- Define the firm's AI vision, roadmap, and governance framework aligned with overall business and investment goals.
- Build and lead a multidisciplinary AI Center of Excellence (CoE) integrating data science, ML engineering, and domain analytics.
- Drive adoption of AI-driven decision-making across investment, operations, and portfolio management.
- Stay ahead of emerging trends in GenAI, automation, and computational life sciences, and identify opportunities for integration.
2. Investment Intelligence & Analytics:

Develop AI/ML models for:
- Market and sector intelligence (price trends, regulatory dynamics, supply chain analytics).
- Deal sourcing, screening, and due diligence.
- Portfolio performance forecasting and value creation tracking.
- Partner with investment teams to embed AI in deal evaluation, scenario modeling, and risk assessment.
3. Pharma & Life Sciences Applications:
Oversee the design and deployment of AI solutions across Pharma API, R&D, and manufacturing value chains:
- Process optimization and yield improvement.
- Predictive quality, compliance, and safety analytics.
- Supply chain and procurement intelligence.
- R&D data mining, molecule clustering, and formulation analytics.
- Collaborate with portfolio companies to implement AI tools for digital transformation and operational excellence.
4. Infrastructure & Technology Leadership:
- Build robust data infrastructure and AI platforms for scalable analytics across the firm and its portfolio.
- Establish data pipelines, APIs, and cloud environments for model training and deployment.
- Implement strong data governance, compliance, and ethical AI standards.
5. Team Building & Stakeholder Engagement:
- Build and manage a high-performing team of data scientists, AI engineers, and domain experts.
- Partner with CIOs, CTOs, and CEOs of portfolio companies to guide AI adoption.
- Represent the firm in AI and pharma innovation forums, partnerships, and collaborations.