Job Description & How to Apply Below
The ideal candidate combines strong analytical expertise, solid experience working with complex datasets, and a deep understanding of pharmaceutical or life sciences environments.
Key Responsibilities
Data Analytics & Modeling
Develop, validate, and deploy predictive and machine learning models to support R&D, clinical trials, manufacturing, and commercial operations.
Analyze large and heterogeneous datasets (clinical, real-world data, lab data, manufacturing data, supply chain).
Perform exploratory data analysis (EDA) and communicate insights to scientific and business stakeholders.
Implement statistical modeling , hypothesis testing, and time-series forecasting as required.
Build dashboards, reports, and visual insights to support decision-making.
Pharma-Specific Activities
Support teams across R&D, clinical development, quality, manufacturing, and regulatory .
Ensure compliance with GxP , data integrity , and internal data governance standards.
Collaborate with SMEs, clinicians, biostatisticians, and lab scientists to interpret results.
Work closely with data engineers to ensure high-quality, well-structured datasets.
Develop automated pipelines for data ingestion, cleaning, transformation, and modeling.
Leverage cloud platforms (AWS, Azure, GCP) for scalable model training and deployment.
Cross-functional Collaboration
Communicate results to scientists, technical teams, and leadership.
Contribute to the development of best practices in data science, documentation, and reproducibility.
Support digital innovation initiatives, AI-driven solutions, and automation projects.
Requirements
Education & Experience
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Bioinformatics , or related field.
3–7 years of experience as a Data Scientist, preferably in the pharmaceutical, biotech, life sciences, or healthcare sectors.
Technical Skills
Strong programming skills in Python (pandas, Num Py, scikit-learn, Tensor Flow/PyTorch).
Experience with SQL and relational databases (PostgreSQL, SQL Server, Oracle).
Experience building and deploying machine learning models.
Familiarity with cloud-based ML environments (AWS Sagemaker, Azure ML, GCP Vertex AI).
Solid understanding of statistics , experimental design , and data modeling .
Experience working with large datasets , ETL pipelines, and data processing frameworks.
Pharma / Life Sciences Skills (Highly Preferred)
Knowledge of GxP , data integrity , and regulatory frameworks.
Familiarity with clinical trial data , lab systems (LIMS), quality systems, or manufacturing datasets (MES, SCADA, historians).
Experience with real-world data (RWD) or real-world evidence (RWE) is an asset.
xpected Compensation Range:
50K-100K The final compensation offered will be determined based on the candidate’s level of experience, skills, and qualifications , in compliance with applicable pay transparency requirements.
Canadian Experience Requirement:
No Canadian work experience is required for this position. Candidates are encouraged to apply regardless of previous Canadian experience.
Artificial Intelligence (AI) Disclosure:
No Artificial Intelligence (AI) tools are used in the screening, assessment, or selection of candidates for this position. All stages of the recruitment process are conducted by human recruiters and hiring managers.
Statement Regarding Job Vacancy:
This position is a Job creation, created…
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