Principal Scientist (Computational Chemistry)

Exelixis • Alameda, CA

13d

About The Position

SUMMARY: We are seeking a Principal Scientist in Computational Chemistry to join our small molecule discovery organization. The successful candidate will bring deep expertise in ligand design and cheminformatics with experience in a pharmaceutical or biotechnology drug discovery setting. Strong communication and collaboration skills are essential, as this position will interact closely with interdisciplinary small molecule discovery project teams. This individual will apply computational and data-driven methods to advance small-molecule discovery projects, collaborating closely with medicinal chemistry, structural biology, pharmacology and biology teams to advance early-stage discovery projects. ESSENTIAL DUTIES AND RESPONSIBILITIES: Apply and develop cheminformatics and machine learning models to enable predictive analytics across the pipeline Lead and contribute to ligand- and structure-based design efforts in active discovery programs Integrate computational & machine learning tools into collaborative platforms (e.g., LiveDesign, Spotfire) to make predictive insights accessible to cross-functional teams Experience in applying machine learning approaches, including protein-ligand/protein-protein co-folding models and generative chemistry models Analyze large data sets and apply cheminformatics techniques to extract design-relevant insights Drive virtual screening campaigns (structure-based, ligand-based, and fragment-based) to support hit identification and lead optimization Communicate results and strategy effectively to project teams and leadership Contribute to the strategic growth of CADD capabilities and help establish a predict-first culture within the organization Maintain notebooks in accordance with company policies and practices EDUCATION & EXPERIENCE: Bachelor’s degree in related discipline and a minimum of 11 years of relevant experience; or, Master’s degree in related discipline and a minimum of 9 years of relevant experience; or, PhD in Computational Chemistry, Cheminformatics or related discipline and a minimum of 5 years of post-doctorate experience; or, Equivalent combination of education and experience. DISCLAIMER The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to the job. #LI-HG1 If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us! Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The base pay range for this position is $150,000 - $213,000 annually. The base pay range may take into account the candidate’s geographic region, which will adjust the pay depending on the specific work location. The base pay offered will take into account the candidate’s geographic region, job-related knowledge, skills, experience and internal equity, among other factors. In addition to the base salary, as part of our Total Rewards program, Exelixis offers comprehensive employee benefits package, including a 401k plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts. Employees are also eligible for a discretionary annual bonus program, or if field sales staff, a sales-based incentive plan. Exelixis also offers employees the opportunity to purchase company stock, and receive long-term incentives, 15 accrued vacation days in their first year, 17 paid holidays including a company-wide winter shutdown in December, and up to 10 sick days throughout the calendar year. DISCLAIMER The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to the job. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.

Requirements

• Bachelor’s degree in related discipline and a minimum of 11 years of relevant experience; or, Master’s degree in related discipline and a minimum of 9 years of relevant experience; or, PhD in Computational Chemistry, Cheminformatics or related discipline and a minimum of 5 years of post-doctorate experience; or, Equivalent combination of education and experience.
• Demonstrated impact on drug discovery projects, from hit ID through lead optimization
• Experience in cheminformatics, data analysis, and machine learning co-folding approaches
• Proficiency with Python/R, RDKit, Pipeline Pilot and ML frameworks
• Proactive mindset to bring open-source/external ML solutions in-house to enable design and decision-making
• Strong expertise in ligand- and structure-based drug design methods, including docking, scaffold hopping, virtual library enumeration, molecular dynamics, and free energy approaches
• Solid understanding of drug-like property optimization (ADME, physicochemical parameters, liabilities)
• Proven track record of effective collaboration with medicinal chemists, biologists, and other stakeholders.
• Strong communication and presentation skills; ability to influence project strategy with computational insights.

Nice To Haves

• Strategic thinker with a passion for applying computational & ML approaches to solve practical problems in drug discovery
• Collaborative team player, able to work seamlessly across disciplines
• Proactive, innovative, and adaptable in a fast-paced environment
• Strong balance of technical depth and project impact orientation
• Strong written, oral, and presentation communication skills

Responsibilities

• Apply and develop cheminformatics and machine learning models to enable predictive analytics across the pipeline
• Lead and contribute to ligand- and structure-based design efforts in active discovery programs
• Integrate computational & machine learning tools into collaborative platforms (e.g., LiveDesign, Spotfire) to make predictive insights accessible to cross-functional teams
• Experience in applying machine learning approaches, including protein-ligand/protein-protein co-folding models and generative chemistry models
• Analyze large data sets and apply cheminformatics techniques to extract design-relevant insights
• Drive virtual screening campaigns (structure-based, ligand-based, and fragment-based) to support hit identification and lead optimization
• Communicate results and strategy effectively to project teams and leadership
• Contribute to the strategic growth of CADD capabilities and help establish a predict-first culture within the organization
• Maintain notebooks in accordance with company policies and practices

Benefits

• comprehensive employee benefits package, including a 401k plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
• Employees are also eligible for a discretionary annual bonus program, or if field sales staff, a sales-based incentive plan
• Exelixis also offers employees the opportunity to purchase company stock, and receive long-term incentives, 15 accrued vacation days in their first year, 17 paid holidays including a company-wide winter shutdown in December, and up to 10 sick days throughout the calendar year

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Ph.D. or professional degree