(Senior) Director of Product, Medicinal Chemistry

Lila Sciences•Cambridge, MA

16d•$184,000 - $294,000

About The Position

Lila Sciences is looking for a (Senior) Director of Product, Medicinal Chemistry, a strategic and visionary product leader who will shape the future of our therapeutic research and development capabilities. You will lead the strategy, roadmap, and execution for a portfolio of products and platforms at the intersection of drug discovery, life science research, and scientific intelligence. This is a high-impact, high-visibility role that requires exceptional leadership, cross-functional orchestration, and a deep understanding of scientific and technical domains.

Requirements

MS, PhD, or equivalent experience in medicinal chemistry, computational chemistry, chemical biology, or a related field with grounding in experimental or in silico small molecule research.
9+ years of experience in product management or platform strategy roles at the intersection of drug discovery, cheminformatics, AI/ML, or scientific software development
3+ years of experience leading and managing cross-functional or product teams, including responsibility for performance, alignment, and mentorship.
Deep understanding of small molecule discovery workflows, including structure-based design, ligand-based modeling, hit finding, lead optimization, ADMET profiling, and synthesis planning.
Familiarity with ML tools for QSAR modeling, HTS triage, retrosynthesis, and generative compound design, as well as common libraries such as RDKit, DeepChem, ASKCOS, or graph-based ML frameworks for chemistry.
Experience defining and executing TPPs for new drug discovery tools, therapeutic products, or scientific data platforms
Experience with strategic planning and portfolio management across extremely complex, multidisciplinary projects
Relentlessly output-focused: driven by impact against our mission and program goals, not by doing tasks, owning decisions, or being in meetings
Outstanding ability to influence across all levels of the organization and drive alignment among technical and executive stakeholders
Highly collaborative and empathetic
High degree of comfort with ambiguity and able to adapt to changing teams and needs
Able to create small, achievable units of work from large, complex, and ambiguous goals
Excellent communication and presentation skills, capable of conveying technical information to any audience with experience presenting to executive leadership and external partners

Nice To Haves

Experience with automated/self-driving labs and experience working with (not necessarily having run) machine learning in drug discovery or life science research
Following the rapid evolution of LLMs and other AI solutions and have thoughts and opinions on how this will impact the future of scientific discovery and product development
Background in partnering with biopharma or research organizations to define collaborative milestones and workflows

Responsibilities

Set and drive the long-term product vision and strategic roadmap for Lila’s chemistry and small molecule discovery and optimization capabilities, closely aligned with company-wide objectives and scientific priorities.
Identify and shape product opportunities grounded in real-world discovery challenges, partnering closely with collaborators, customers, and strategic stakeholders to ensure relevance and downstream value.
Define target product profiles (TPPs) and develop product strategies that enable intelligent, iterative optimization of chemical matter using Lila's SSI platform.
Own product portfolio planning and prioritization across multiple high-impact initiatives and experiments, balancing long-term vision with near-term deliverables
Represent product leadership across internal and external stakeholders, including executive leadership, partners, and the broader scientific community
Drive product execution from concept to launch while leading cross-functional teams across science, machine learning, automation, and data engineering
Manage resource allocations (budget, compute, lab access, people) and decisions against a complex set of priorities (e.g., focus of experiments between AI model training and near-term partnership requests)
Define clear success metrics and milestones, and clearly communicate progress against them to the company, including executive leadership