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Associate Director, Machine Learning

Job DescriptionThe Computational Toxicology group within the Nonclinical Drug Safety (NDS) division at our company is actively seeking an enthusiastic AI/ML data Scientist to contribute to the discovery and development of effective, safer therapeutics for patients. This key role will be instrumental in supporting NDS's broader vision to be at the forefront of AI/ML application and data science methodologies towards drug safety prediction.Note: This position is available in South San Francisco, CA or West Point, PA based on candidate preference.Key Responsibilities:• Individual contributor role, requiring mentorship of junior scientists• Prepare ML model-ready dataset by curation, integration, and harmonization of multimodality data from the internal and external domains, in partnership with the subject-matter experts within NDS and our company's Research IT teams• Apply probabilistic, neural networks ML models, and generative AI methods to inform prioritization for chemistry and toxicology resources• Leverage LLM and generative AI models (e.g. GAN, VAE) to understand mechanisms of toxicity, identify molecules with desired drug-like properties, to prioritize animal resources• Work collaboratively with colleagues across multiple sites and functional areas to deploy, utilize, and increase the visibility of ML approaches in selection of chemical series with a high probability of success, and enable prioritization of in vivo resources• Upscale NDS staff on the utility of predictive AI/ML approaches in drug safety• Stay abreast with new AI approaches and regulatory landscape in the field of predictive toxicologyQualifications:Education:• Ph.D. (with 4+ years) or Master’s (with 8+ years) in computer science, computational biology, cheminformatics or related data science fields with relevant years of experienceRequired Experience and Skills:• Proven experience in applying traditional machine learning methods as well as, Deep Neural Networks, Generative AI and LLM on data from pharmaceutical or other industry sectors• Fluency in python, R programing, standard python packages like Pandas, NumPy, Matplotlib, and ML frameworks such as TensorFlow, Pytorch• Experience with version control and related code reproducibility practices such as git documentation• Experience with Linux and high-performance or cloud computing environments (e.g., AWS) and data lake platforms (e.g., Databricks)• Self-motivated with a high level of autonomy• Exceptional communication, presentation, and collaboration skills to effectively distill complex technical concepts for a broad range of stakeholders is necessary• High interpersonal skills with a collaborative mindset• Domestic travel 10-20% is requiredPreferred Experience and Skills:• Experience with ML models deployment frameworks such as ML-Ops• Prior experience working in a multidisciplinary environment within the Pharmaceutical Industry, and other healthcare sectorsNOTICE FOR INTERNAL APPLICANTSIn accordance with Managers' Policy - Job Posting and Employee Placement, all employees subject to this policy are required to have a minimum of twelve (12) months of service in current position prior to applying for open positions.If you have been offered a separation benefits package, but have not yet reached your separation date and are offered a position within the salary and geographical parameters as set forth in the Summary Plan Description (SPD) of your separation package, then you are no longer eligible for your separation benefits package. To discuss in more detail, please contact your HRBP or Talent Acquisition Advisor.#EligibleforERP#DATA2025Current Employees apply HERECurrent Contingent Workers apply HEREUS and Puerto Rico Residents Only:Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities. Please click here if you need an accommodation during the application or hiring process.We are an Equal Opportunity Employer, committed to fostering an inclusive and diverse workplace. All qualified applicants will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status, or other applicable legally protected characteristics. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit:EEOC Know Your RightsEEOC GINA Supplement​Pay Transparency NondiscriminationWe are proud to be a company that embraces the value of bringing diverse, talented, and committed people together. The fastest way to breakthrough innovation is when diverse ideas come together in an inclusive environment. We encourage our colleagues to respectfully challenge one another’s thinking and approach problems collectively.Learn more about your rights, including under California, Colorado and other US State ActsU.S. Hybrid Work ModelEffective September 5, 2023, employees in office-based positions in the U.S. will be working a Hybrid work consisting of three total days on-site per week, Monday - Thursday, although the specific days may vary by site or organization, with Friday designated as a remote-working day, unless business critical tasks require an on-site presence.This Hybrid work model does not apply to, and daily in-person attendance is required for, field-based positions; facility-based, manufacturing-based, or research-based positions where the work to be performed is located at a Company site; positions covered by a collective-bargaining agreement (unless the agreement provides for hybrid work); or any other position for which the Company has determined the job requirements cannot be reasonably met working remotely. Please note, this Hybrid work model guidance also does not apply to roles that have been designated as “remote”.The Company is required to provide a reasonable estimate of the salary range for this job in certain states and cities within the United States. Final determinations with respect to salary will take into account a number of factors, which may include, but not be limited to the primary work location and the chosen candidate’s relevant skills, experience, and education.Expected US salary range:$167,700.00 - $263,900.00Available benefits include bonus eligibility, long term incentive if applicable, health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and sick days. A summary of benefits is listed here.San Francisco Residents Only: We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance OrdinanceLos Angeles Residents Only: We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring OrdinanceSearch Firm Representatives Please Read CarefullyMerck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.Employee Status:RegularRelocation:Domestic/InternationalVISA Sponsorship:YesTravel Requirements:10%Flexible Work Arrangements:Not ApplicableShift:1st - DayValid Driving License:NoHazardous Material(s):NoRequired Skills:Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Software Development, Stakeholder Relationship Management, Waterfall Project ManagementPreferred Skills:Job Posting End Date:01/31/2025• A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.Requisition ID:R314301
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What You Should Know About Associate Director, Machine Learning, Merck

Are you passionate about pushing the boundaries of drug safety through innovative technology? As the Associate Director of Machine Learning at our company in West Point, PA, you'll play a pivotal role in the Computational Toxicology group within the Nonclinical Drug Safety division. This exciting position calls for an enthusiastic AI/ML data scientist who is ready to dive into the world of therapeutics and make a real difference for patients. Here, you'll prepare machine learning model-ready datasets by curating and harmonizing data from various sources, working collaboratively with our Research IT teams and subject matter experts. You'll get to apply cutting-edge ML methods—including probabilistic models and generative AI techniques—to prioritize resources in chemistry and toxicology. Additionally, you will mentor junior scientists and help scale our staff's understanding of predictive AI/ML approaches. A typical day might involve analyzing toxicology mechanisms or deploying machine learning models to identify promising drug candidates. Your experience with programming languages like Python and R, as well as familiarity with ML frameworks such as TensorFlow and PyTorch, will set you up for success. Join our team and stay at the forefront of AI application in drug safety. We’re looking for someone who's both self-motivated and eager to work in a collaborative environment. Come be a part of something transformational!

Frequently Asked Questions (FAQs) for Associate Director, Machine Learning Role at Merck
What are the essential qualifications for the Associate Director, Machine Learning position at our company?

To qualify for the Associate Director, Machine Learning role at our company, candidates should have a Ph.D. in computer science, computational biology, or a related field with at least 4 years of experience, or a Master’s degree with 8 years of experience. Proven skills in machine learning techniques, especially deep neural networks and generative AI, are crucial, alongside fluency in Python and R, and experience with cloud computing environments like AWS.

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What key responsibilities does the Associate Director, Machine Learning have?

The Associate Director, Machine Learning is responsible for curating and integrating multimodal datasets, applying advanced ML models to inform toxicology resource prioritization, and collaborating across multiple functions to enhance ML's visibility in drug safety processes. Mentorship of junior scientists and upscaling staff on predictive AI/ML approaches are also significant aspects of this role.

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How does the Associate Director, Machine Learning role impact drug safety predictions at our company?

In the Associate Director, Machine Learning position at our company, the impact on drug safety predictions is substantial. By applying advanced machine learning models and generative AI, you will help identify potential toxicity early in the drug development process, enabling safer therapeutic options for patients and optimizing the resources allocated in drug safety studies.

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What programming skills are necessary for the Associate Director, Machine Learning role?

For the Associate Director, Machine Learning role at our company, proficiency in programming languages such as Python and R is essential. Experience with machine learning frameworks like TensorFlow and PyTorch, along with familiarity with data management tools like Pandas and NumPy, will be crucial to effectively analyze and interpret data.

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What is the work environment for the Associate Director, Machine Learning position at our company?

The work environment for the Associate Director, Machine Learning role at our company is collaborative and dynamic. The position allows for a hybrid work model, promoting flexibility while working closely with cross-site teams focused on contributing to the company's drug safety initiatives. Candidates should expect a combination of independent and collaborative work, often engaging in both remote and on-site activities.

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Common Interview Questions for Associate Director, Machine Learning
Can you describe your experience with machine learning models in drug safety?

When answering this question, highlight specific projects where you applied machine learning models to drug safety data. Discuss the types of models used, the datasets involved, and any outcomes that relate to identifying toxicological risks or predicting safety profiles.

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What generative AI techniques have you applied in your previous work?

Share details about your experience with generative AI, such as GANs or VAEs. Explain how you utilized these techniques in practical scenarios, emphasizing their effectiveness in modeling complex chemical properties or predicting outcomes in drug discovery.

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How do you approach dataset preparation for machine learning?

Explain your systematic approach to preparing datasets, including cleaning, harmonizing, and integrating multimodal data. Discuss tools and techniques you've used to ensure the quality and relevance of data for model training.

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What is your experience working in collaborative environments?

Provide examples of past collaborative experiences, detailing how you communicated complex technical concepts to non-experts and worked seamlessly with cross-functional teams to achieve project goals.

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How do you stay updated on the latest trends and regulations in predictive toxicology?

Share your strategies for staying informed, such as attending relevant conferences, participating in online forums, or reading industry journals. Highlight any specific sources or networks that provide valuable insights on predictive toxicology.

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What types of machine learning frameworks are you proficient in?

Enumerate the machine learning frameworks you have expertise in, such as TensorFlow, PyTorch, or Scikit-learn. Provide examples of how you've employed these frameworks in developing and deploying machine learning models.

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Describe a challenging project you faced and how you overcame the obstacles.

Choose a relevant project where you encountered significant challenges. Discuss the specific obstacles, how you approached finding solutions, and the steps you took to ensure the project's success, focusing on teamwork, adaptability, and problem-solving skills.

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Can you explain your experience with version control practices?

Discuss your familiarity with version control systems like Git. Share how you've used these tools in past projects to maintain project integrity, collaborate with others, and ensure reproducibility of code.

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What role does mentorship play in your work?

Describe your approach to mentorship, including any experiences you've had in guiding junior scientists. Emphasize the importance of knowledge sharing and building team expertise in machine learning applications.

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How do you prioritize tasks when working on multiple projects?

Share your techniques for task prioritization, such as setting clear goals, utilizing project management tools, and maintaining communication with stakeholders to ensure alignment on project objectives.

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It all comes back to inventing for life We are all inventors here, no matter the role or title. We rise to any challenge in pursuit of better health outcomes. Everything we do, in and out of the laboratory, is based on our deep appreciation for l...

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