Data Scientist - Real World Data - Data42

Healthcare Businesswomen’s Association; Dublin; On-site; CAD 110,000 - 150,000; Full time

Job summary

A leading company in healthcare is seeking an RWD SME / RWE Execution Data Scientist to innovate and analyze Real-World Data using advanced methodologies. The ideal candidate will have a PhD/MSc in a quantitative field and significant experience in AI/ML, working collaboratively to tackle complex medical data challenges.

Qualifications

• 8+ years of relevant experience in Data Science (or 4+ years post-PhD).
• Extensive experience with Statistical and Machine Learning techniques.
• Strong collaboration and communication skills across global teams.

Responsibilities

• Collaborate with R&D stakeholders to implement data solutions.
• Define and execute advanced analytical approaches for RWD analysis.
• Stay current with emerging applications and trends in analytics.

Skills

Applied Machine Learning

Artificial Intelligence (AI)

Data Analysis

Deep Learning

Data Management

Education

PhD or MSc. in a quantitative discipline

Tools

Python

SQL

AWS

Job description

Job Description Summary

Are you passionate about the intersection of data, technology, and science, and excited by the potential of Real-World Data (RWD) and AI? Do you thrive in collaborative environments and aspire to contribute to groundbreaking medical insights? If so, join the data42 team at Novartis!

At Novartis, we reimagine medicine by leveraging advanced analytics and extensive data resources. Our data42 platform provides access to high-quality, multi-modal preclinical and clinical data, along with RWD, enabling the development of AI/ML models and health insights. Our global team of data scientists and engineers uses this platform to uncover novel insights and support drug development decisions.

As an RWD SME / RWE Execution Data Scientist, you will focus on executing innovative methodologies and AI models to analyze RWD on the data42 platform. You will be the authority on leveraging diverse RWD modalities to understand patient populations, biomarkers, and drug targets, accelerating the development of life-changing medicines.

Job Description

Duties and Responsibilities:

1. Collaborate with R&D stakeholders to co-create and implement innovative, scalable, and automatable data and technology solutions aligned with data42 strategy.
2. Serve as a data SME, understanding RWD modalities, vocabularies (LOINC, ICD, HCPCS, etc.), non-traditional RWD (Patient-reported outcomes, Wearables, Mobile Health Data), and their applications in conjunction with clinical, omics, pre-clinical, and commercial data.
3. Contribute to data strategy implementation such as Federated Learning, tokenization, data quality frameworks, regulatory requirements (e.g., HL7 FHIR formats, Sentinel initiative), conversion to common data models (OMOP, FHIR, SEND), FAIR principles, and integration with enterprise catalog.
4. Define and execute advanced analytical approaches and research methodologies (including industry trends) supporting exploratory and regulatory research questions using AI models for RWD analysis across the Research, Development, and Commercial continuum.
5. Stay current with emerging applications and trends, driving the development of advanced analytics for data42 across the RWE lifecycle, from ideation to study design and execution.
6. Work effectively across various cross-functional teams across locations and domains to address complex business problems with measurable impact.
7. Draft and edit high-level research documents (proposals, protocols, statistical analysis plans). [optional]
8. Knowledge of governance, ethical, and privacy considerations. [optional]

Ideal Candidate Profile:

1. PhD or MSc. in a quantitative discipline (e.g., Computer Science, Physics, Statistics, Epidemiology) with proven AI/ML expertise.
2. 8+ years of relevant experience in Data Science (or 4+ years post-PhD).
3. Extensive experience with Statistical and Machine Learning techniques: Regression, Classification, Clustering, Design of Experiments, Monte Carlo Simulations, Statistical Inference, Feature Engineering, Time Series Forecasting, Text Mining, NLP, LLMs, and multi-modal Generative AI.
4. Good to have skills: Stochastic models, Bayesian models, Markov Chains, Optimization, Deep Learning on structured and unstructured data, Recommender Systems.
5. Proficiency in tools: Python, R (optional), SQL; experience with PowerBI, R-Shiny, Flask, or similar is advantageous.
6. Knowledge of data standards (OHDSI OMOP, FHIR, HL7) and best practices.
7. Nice to have: Foundry, big data programming, experience with AWS, DataBricks, SnowFlake.
8. Strong collaboration and communication skills across global teams.
9. High learning agility and industry awareness.
10. Optional: Experience in biomedical R&D in pharma is a bonus.

Skills Desired

Applied Machine Learning, Artificial Intelligence (AI), AWS Data Pipeline, Bayesian Models, Classification, Clinical Trials, Common Data Model (CDM), Data Analysis, Data Integration, Data Science, Data Management, Deep Learning, FHIR, Federated Learning, HL7 Standards, Markov Chains, and more.