Job Description
Our Artificial Intelligence Machine Learning (AI/ML) capabilities are critical accelerators to our mission to delivering towards inventing new medicines that save and improve lives. Core to the Data, AI, and Genome Sciences (DAGS) function is an AI/ML-first approach to improving target and biomarker discovery, validation and selection and elucidating complex disease mechanisms. Your work will contribute to the advancement of our understanding of complex diseases and support the development of innovative therapeutic strategies. You will be part of a cross-functional team of computational biologists, bioinformaticians, data scientists, software engineers, and machine learning engineers that strive to identify therapeutic targets.
Primary Responsibilities:
Collaborate with cross-functional teams to identify research questions, data requirements, and develop appropriate solutions.
Develop methods and models tailored to the complexities of biological experiments and rigorously benchmark against off-the-shelf techniques.
Interpret the results produced by the computational approaches and use them to drive better understanding of disease biology and design of better therapeutics.
Stay up to date with the latest advancements in machine learning and statistics and apply relevant advancements to improve existing methodologies and models.
Publish research findings in relevant conferences and journals, and actively contribute to the scientific community through knowledge sharing and collaborations.
Required Experience and Skills:
PhD in Computer Science, Statistics, Physics, or a related field and 0-3+ years experience, MS and 4+ years of experience, or BS and 7+ years of experience.
Strong background in statistical machine learning, probabilistic graphical models, uncertainty quantification, and familiarity with foundation models.
Demonstrated experience with inference in probabilistic models.
Strong proficiency in Python, and experience with standard deep learning frameworks like PyTorch and JAX.
Excellent communication skills and ability to work collaboratively in multi-disciplinary team.
Preferred Experience and Skills:
Demonstrated experience with: simulation based inference (approximate Bayesian computation and modern alternatives), sequential and batch optimal experimental design, active learning, Bayesian optimization.
Relevant publications in scientific journals and experience contributing to research communities, including NeurIPS, ICML, ICLR, etc.
Familiarity with biological data is a strong plus.
Interest in life sciences problems and disease biology, and willing to learn from and teach others.
#EligibleforERP
Current Employees apply HERE
Current Contingent Workers apply HERE
US and Puerto Rico Residents Only:
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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:
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U.S. Hybrid Work Model
Effective 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”.
San Francisco Residents Only: We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance
Los 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 Ordinance
Search Firm Representatives Please Read Carefully
Merck & 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:
Regular
Relocation:
Domestic/International
VISA Sponsorship:
Yes
Travel Requirements:
10%
Flexible Work Arrangements:
Hybrid
Shift:
Not Indicated
Valid Driving License:
No
Hazardous Material(s):
n/a
Required Skills:
Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Software Development, Stakeholder Relationship Management, Waterfall Model
Preferred Skills:
Job Posting End Date:
12/23/2024
*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.
