Associate Director, Data Science

Novartis

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Cambridge, MA
Salary
$160,300–$297,700 / yr
Posted
81 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $203k
This role $229k
$132k most similar roles pay here $315k

This role pays more than 72% of similar roles. Most pay $164,531–$240,600 — the shaded band above. At the midpoint, this role pays about $229k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Novartis

Novartis is a global biopharmaceutical company that researches, develops, manufactures, and markets prescription drugs in areas including oncology, immunology, neuroscience, and cardiology. Industry: Biopharmaceuticals

Novartis currently has 26 open roles on FindRole.

Listed pay typically runs $194,600–$361,400 across 26 roles with salary data.

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View all roles at Novartis

At a glance

TL;DR · Associate Director, Data Science

Role Posting Log in to save

Associate Director, Data Science joins the Pharmacokinetic Sciences Modeling & Simulation team within Translational Medicine to advance drug discovery through data-driven methodologies. This role involves integrating advanced analytics, machine learning, and mechanistic modeling—such as PK/PD and QSP—to transform large-scale experimental datasets into actionable insights for clinical decision-making. The successful candidate will build hybrid modeling pipelines, develop scalable workflows, and design tools to link molecular structures and pharmacological outcomes across various modalities. Key technical requirements include proficiency in Python or R, experience with libraries like scikit-learn, PyTorch, or Keras, and expertise in statistics and data visualization. The role focuses on the specific domain of model-informed drug development, where the primary objective is to bridge biology and clinical outcomes by utilizing AI-augmented modeling to guide compound progression and prioritize candidates throughout the development lifecycle.

Skills

Machine Learning Artificial Intelligence Python R scikit-learn PyTorch Keras Data Science Statistics SQL Pandas Deep Learning Time Series Analysis Data Visualization Exploratory Data Analysis Version Control GenAI NLP Biostatistics

What you'll do

  • Integrate mechanistic modeling and machine learning to bridge biological data with clinical outcomes.
  • Design and implement hybrid modeling pipelines where mechanistic simulations generate features for machine learning models.
  • Translate model-derived biomarkers and mechanistic states into clinically relevant prediction tools.
  • Develop scalable, reproducible workflows integrating data science, mechanistic modeling, and in-house software tools.
  • Define and implement project-specific in silico modeling and data strategies to guide compound progression.
  • Apply advanced analytics to link molecular structure, ADME properties, and pharmacological outcomes across modalities.
  • Drive the adoption of in silico models and tools to accelerate decision-making in drug development.
  • Communicate complex modeling insights to multidisciplinary teams to influence clinical and research decisions.

What we're looking for

  • Advanced degree in life sciences or a quantitative discipline such as data science, computational biology, or pharmacometrics.
  • PhD with 5+ years of relevant experience in drug discovery or development.
  • MSc with 8+ years of relevant experience in drug discovery or development.
  • Strong expertise in machine learning, statistics, and data science methods.
  • Proficiency in Python and/or R including software development best practices like version control and testing.
  • Experience with machine learning libraries such as scikit-learn, PyTorch, or Keras.
  • Strong understanding of ADME, PK/PD, and translational modelling concepts.
  • Fluency in English for oral and written communication across multidisciplinary teams.

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