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.