AstraZeneca
Healthcare
SeniorDataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Scientist at AstraZeneca. Skills: Data science, Machine learning, AI research, Modelling. Frame problems. Develop hypotheses”
What You'll Achieve.
Deliver measurable outcomes
Industry & Context.
Problem framing; Hypothesis development
What They're Looking For.
Must Have
Bachelors Degree or equivalent experience, Experience in classification algorithms, Experience in regression algorithms, Experience in k-means clustering, Experience in support vector machines, Experience in random forests, Experience in neural networks, Experience in deep learning, Knowledge in advanced statistical modelling, Understanding of algorithm design, Understanding of algorithm development, Understanding of algorithm optimization, Understanding of algorithm scaling, Understanding of algorithm applications, Experienced in Python, Familiarity with database systems, Familiarity with SQL, Familiarity with NoSQL, Familiarity with graph databases
Nice to Have
Masters Degree preferred, Comfortable working in high performance computing, Comfortable working in cloud environment, Experience with large datasets, Experience with distributed computing, Experience working with real world data
What You'll Do.
Build predictive models
Build computational methods
Contribute to publications
Contribute to conferences
How You'll Work.
Team & Collaboration
Cross-functional peers; Scientific collaborators; Matrixed projects
Communication Scope
Written communication; Verbal communication; Present findings
Full Job Description
Role based in Barcelona 3 days at office / 2 days at home You will join a team that embraces a digital mindset and treats data as a core asset. From day one, you will contribute to high-priority projects where evidence quality, reproducibility and impact matter. As your solutions mature, you will see them adopted across programs and scaled to deliver measurable outcomes for patients and the business. Accountabilities: \- Problem Framing and Hypothesis Development: Translate unstructured scientific and business problems into well-defined data problems, analytical approaches and testable hypotheses. \- Advanced Modelling and AI Research: Develop and implement models and methods across classification, regression, clustering, LLMs and simulation to interrogate clinical and business datasets within defined project scope. \- Decision Support and Insight Generation: Build predictive models and computational methods that guide decision-making, communicating results with appropriate uncertainties and limitations within agreed frameworks. \- Multimodal Data Integration: Integrate and process data from multiple sources and modalities to enable robust, scalable analyses that drive tangible value. \- Stakeholder Partnership: Build effective relationships with project managers, cross-functional peers, and scientific collaborators to ensure solutions are understood, trusted and used. \- Communication and Dissemination: Clearly and objectively present findings to technical and non-technical audiences; where appropriate, contribute to publications or conferences. \- Software Engineering and Compliance: Develop, implement and maintain tools and algorithms using a software engineering approach that meets regulatory and evidential requirements within established governance. \- Cross-Functional Contribution: Provide data science support to matrixed projects, executing defined solutions that deliver measurable outcomes for AstraZeneca. Essential Skills/Experience: \- Bachelors Degree (or
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