Remote Senior Pharma Data Scientist — ML for Drug Discovery

Placements24; Benoni; Hybrid; ZAR 1,976,000 - 3,129,000; Full time

Benefits offered by this job

Health insurance

Home office stipend

Training & conferences

Job summary

Placements24 is seeking a Senior Pharmaceutical Data Scientist for a fully remote role. Leverage advanced data analytics, ML, and statistical modeling to drive insights from clinical trial, real-world, and manufacturing data.

Collaborate with biologists, clinicians, and data engineers to translate scientific questions into data problems, develop dashboards, ensure data integrity, and stay current with industry advances.

Qualifications

• Ph.D. or Master’s in Data Science, Statistics, Computer Science, Bioinformatics, Computational Biology, or related quantitative field.
• Minimum of 5-7 years of experience in data science or related analytical role, with pharma/healthcare exposure.
• Proven expertise in statistical modeling, ML algorithms (regression, classification, clustering, deep learning) and experimental design.
• Proficiency in Python or R and libraries (scikit-learn, TensorFlow, PyTorch, pandas).

Responsibilities

• Develop and implement advanced statistical models and ML algorithms for large-scale pharmaceutical datasets.
• Identify trends and insights to inform drug discovery, development, and commercialization.
• Design and run experiments for model validation and performance evaluation.
• Collaborate with biologists, chemists, clinicians, and domain experts to translate questions into data science problems.
• Develop data visualizations and dashboards to communicate findings clearly.
• Contribute to data infrastructure, pipelines, and best practices.

Skills

Python

R

SQL

Machine Learning

TensorFlow

PyTorch

Big data

Pandas

Education

PhD or Master's in Data Science

Tools

AWS

Azure

GCP

Spark

NoSQL

Job description

Placements24 is seeking a Senior Pharmaceutical Data Scientist for a fully remote role. Leverage advanced data analytics, ML, and statistical modeling to drive insights from clinical trial, real-world, and manufacturing data.

Collaborate with biologists, clinicians, and data engineers to translate scientific questions into data problems, develop dashboards, ensure data integrity, and stay current with industry advances.