Associate Director, Data Science

Novartis

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Cambridge, MA
Salary
$160,300–$297,700 / yr
Employment
Full-time
Posted
127 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $207k
This role $229k
$139k $315k
below market most similar roles pay here above market

This role pays more than 70% of similar roles. Most pay $174,475–$239,630 — the blue band above. At the midpoint, this role pays about $229k versus about $207k 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 17 open roles on FindRole.

Listed pay typically runs $176,400–$327,600 across 17 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

The Associate Director, Data Science joins the PKS Modeling & Simulation team within Translational Medicine to advance data-driven drug discovery. This role involves integrating advanced analytics, machine learning, and mechanistic modeling to transform large-scale experimental datasets into actionable insights. You will develop hybrid modeling pipelines where mechanistic simulations generate features for machine learning models, specifically focusing on PK/PD and QSP. Key responsibilities include designing scalable workflows, translating model-derived biomarkers into clinically relevant predictions, and driving AI-augmented modeling approaches to guide compound progression. Required skills include proficiency in Python and R, experience with libraries like scikit-learn, PyTorch, or Keras, and expertise in statistics and data visualization. The work addresses the technical challenge of bridging biology and clinical outcomes through robust, interpretable in silico methods across various disease areas.

Skills

Machine Learning Python R Deep Learning Mechanistic Modelling PK/PD QSP PyTorch Keras scikit-learn Pandas SQL Data Science Statistics Data Visualization Exploratory Data Analysis Version Control NLP GenAI

What you'll do

  • Integrate mechanistic modelling and machine learning to bridge biology and clinical outcomes.
  • Design and implement hybrid modelling pipelines where mechanistic simulations generate features for machine learning models.
  • Translate model-derived biomarkers and mechanistic states into clinically relevant predictions and decision-support tools.
  • Develop scalable, reproducible workflows integrating data science, mechanistic modelling, and in-house tools.
  • Define and implement project-specific in silico modelling and data strategies aligned with key decision questions.
  • Apply data mining and advanced analytics to link molecular structure, ADME properties, and pharmacological outcomes.
  • Drive the adoption of in silico models and tools to accelerate compound progression and clinical decision-making.

What we're looking for

  • Advanced degree in life sciences or a quantitative discipline.
  • PhD with 5+ years or MSc with 8+ years of relevant experience in drug discovery or development.
  • Strong expertise in machine learning, statistics, and data science methods.
  • Demonstrated experience applying reproducible data science approaches to drug discovery or development.
  • Experience combining mechanistic modelling and data-driven approaches (preferred).
  • Strong understanding of ADME, PK/PD, and/or translational modelling concepts.
  • Proficiency in Python and/or R, including software development best practices.
  • Experience with machine learning libraries such as scikit-learn, PyTorch, or Keras.

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