Senior Scientist I-II, ML Molecular Optimization

AbbVie

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
North Chicago, IL
Salary
$109,500–$208,500 / yr
Posted
19 days ago
Freshness
Confirmed live yesterday
Closes
Oct 8, 2126
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $213k
This role $159k
$91k $281k
below market most similar roles pay here above market

This role pays less than 89% of similar roles. Most pay $177,250–$248,375 — the blue band above. At the midpoint, this role pays about $159k versus about $213k for comparable roles.

Based on 240 similar postings.

Employer

About AbbVie

AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.

AbbVie currently has 145 open roles on FindRole.

Listed pay typically runs $109,500–$208,500 across 140 roles with salary data.

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View all roles at AbbVie

At a glance

TL;DR · Senior Scientist I-II, ML Molecular Optimization

Role Posting Log in to save

The Senior Scientist I-II, ML Molecular Optimization joins the team to advance AI-enabled drug discovery for the small molecule portfolio. This role involves identifying, developing, optimizing, and deploying machine learning approaches to guide molecular optimization. You will collaborate with medicinal chemists and biologists to establish quantitative targets, build predictive models for small molecule binding affinities and pharmacological properties, and benchmark performance while accounting for model uncertainty. Key responsibilities include deploying models for team accessibility and communicating complex rationales to non-specialists. Required expertise includes machine learning, deep learning, and cheminformatics, specifically regarding molecular generation and pose prediction. You must possess strong programming skills in Python using numpy, pandas, and scikit-learn, alongside proficiency in cloud computing and architectures like GNN, CNN, RNN, Transformer, GAN, and active learning.

Skills

Machine Learning Deep Learning Python Cheminformatics Numpy Pandas Scikit-learn GNN CNN RNN Transformer GCNN MPNN GAN Active Learning Cloud Computing Generative Models

What you'll do

  • Establish quantitative targets with project teams to drive molecular optimization.
  • Identify models to predict small molecule binding affinities and pharmacological properties.
  • Build and benchmark predictive models for specific project endpoints.
  • Select optimal models for project queries using data-driven approaches that account for uncertainty.
  • Communicate model selection rationales clearly to project teams.
  • Deploy models to ensure accessibility for collaborative ideation and optimization.

What we're looking for

  • Degree in chemistry, computer science, machine learning, cheminformatics, or chemical engineering.
  • 10+ years of experience for BS, 8+ years for MS, or 0+ years for PhD in developing machine learning models for chemical and biological data.
  • 12+ years of experience for BS, 10+ years for MS, or 4+ years for PhD in developing machine learning models for chemical and biological data.
  • Expertise in developing, implementing, and deploying programs using Machine Learning, Deep Learning, and Cheminformatics.
  • Expertise in AI/ML-enabled molecular generation, pose prediction, affinity prediction, and prediction of pharmacological properties.
  • Strong programming skills in Python and the data science stack including numpy, pandas, and scikit-learn.
  • Proficiency with cloud computing capabilities and ability to implement, debug, and maintain computational tools.
  • Familiarity with deep learning architectures like GNN, CNN, RNN, Transformer, GCNN, and MPNN, and paradigms like GAN and active learning.

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