Own scientific direction across modalities, data, and workflows, Define which drug discovery problems, modalities, and data types Apheris prioritizes with our product offerings., Guide how applications and models should evolve to remain maximally useful to pharma teams., Balance scientific ambition with practicality and adoption in customer environments., Own the “why” and high-level structure of new scientific initiatives and proposals, while partnering closely with internal ML and science teams on detailed methodology and execution., Build and lead an established, growing computational drug discovery team, Lead a core team already in place, including a senior medicinal chemist and a forward-deployed scientist, both with top-tier academic and industry backgrounds., Hire and integrate 1–2 additional team members over the next 6–9 months as customer demand scales., Set clear direction and execution standards across all customer-facing programs., Oversee scientific quality and program enablement rather than personally owning detailed technical implementation., Act as the senior counterpart to pharma R&D leaders, Engage comfortably and credibly with leading computational drug discovery scientists at large pharmaceutical companies., Lead deep scientific discussions on program strategy, model application, and decision-making trade-offs., Provide authoritative guidance grounded in real-world drug discovery experience., Serve as the visible scientific face to customers, including at senior leadership level., Represent Apheris in the ecosystem, Build trust and credibility through talks and direct engagement with the community., Strengthen Apheris’ reputation as a serious, neutral partner for computational drug discovery., Contribute to strategic narratives around why federated and AI-driven approaches enable better drug programs, supported by internal ML and science teams for technical manuscript writing., Oversee and challenge program-level recommendations to ensure strategic alignment and scientific rigor., Drive hands-on scientific leadership across customer programs, Give concrete, actionable scientific direction to application scientists and customer teams., Ensure recommendations reflect how discovery programs are actually run, not theoretical workflows., Review and challenge analyses, models, and conclusions to maintain a high scientific bar., Partner closely with Product and ML/AI teams, Translate customer needs and scientific insight into clear product and model requirements., Shape prioritization decisions by grounding them in customer impact and discovery realities., Act as a scientific bridge between customers and internal development teams.