Associate Director, Data Science

Novartis

Hybrid

Quick summary

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

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $171k
This role $229k
$98k most similar roles pay here $319k

This role pays more than 83% of similar roles. Most pay $133,750–$208,800 — the shaded band above. At the midpoint, this role pays about $229k versus about $171k 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 16 open roles on FindRole.

Listed pay typically runs $160,300–$297,700 across 16 roles with salary data.

Most-posted roles

View all roles at Novartis

At a glance

TL;DR · Associate Director, Data Science

Role Posting Log in to save

As an Associate Director in Novartis’s Pharmacokinetic Sciences Modeling & Simulation team, you will lead the integration of advanced analytics and machine learning with mechanistic modeling to drive drug discovery and development. Your daily responsibilities include designing hybrid modeling pipelines that combine mechanistic simulations with machine learning models to generate actionable insights for decision-making across various stages of clinical development. You will also develop scalable workflows using in-house tools and ensure the adoption of these methodologies within multidisciplinary teams, contributing to departmental strategy and innovation in AI-augmented approaches. Essential skills include expertise in Python or R, proficiency with machine learning libraries like scikit-learn and PyTorch, and a strong background in ADME, PK/PD, and translational modeling concepts. This role requires collaboration across Translational Medicine and Data & Digital teams to integrate diverse datasets for enhanced decision-making efficiency.

Skills

Python R scikit-learn PyTorch Keras Pandas SQL Machine Learning Deep Learning Time Series Analysis Data Visualization Statistical Analysis Artificial Intelligence Graph Algorithms Nlp CI/CD

What you'll do

  • Develop and implement hybrid modelling pipelines combining machine learning with mechanistic simulations.
  • Translate model-derived biomarkers into clinically relevant predictions for decision-making tools.
  • Drive innovation in AI-augmented modelling approaches to enhance mechanistic understanding rigorously.
  • Define project-specific in silico strategies aligned with key decision questions throughout drug development.
  • Integrate diverse datasets from preclinical, clinical, and external sources to support translational programs.
  • Stay current with advances in AI/ML and apply them to improve efficiency and scientific impact.

What we're looking for

  • Advanced degree in life sciences or quantitative discipline with 5+ years of relevant experience.
  • Strong expertise in machine learning, statistics, and data science methods.
  • Experience combining mechanistic modelling and data-driven approaches.
  • Proficiency in Python/R and machine learning libraries like scikit-learn, PyTorch, Keras.
  • Ability to translate complex analytical concepts into clear, actionable insights for decision-making.
  • Strong collaboration and communication skills across multidisciplinary teams.
  • Fluency in English (oral and written).

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