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AstraZeneca is hiring for an fresher entry level Data Scientist role in India




Position:

  • Data Scientist

  • Entry-level, suitable for candidates with 0 to 3 years of experience

  • Focus on real-world data application, AI models, and ML deployment

  • Opportunity to contribute to healthcare innovation using modern tools

Company:

  • AstraZeneca

  • A globally recognized biopharmaceutical company

  • Known for developing and delivering life-changing medicines

  • Active in over 100 countries with a strong presence in India

Location:

  • Bengaluru, Karnataka, India

Job type:

  • Full-time

Job mode:

  • Hybrid (minimum 3 days/week in office)

Job requisition id:

  • R-221088

Years of experience:

  • 0 to 3 years


Company Description:

  • AstraZeneca is a global leader in the biopharmaceutical space, known for its commitment to advancing science and healthcare.

  • The company focuses on prescription medicines across Oncology, Cardiovascular, Renal, Metabolism, and Respiratory areas.

  • With over 10,000 employees in research and development, AstraZeneca is constantly innovating.

  • The company fosters a collaborative culture and values diversity, inclusion, and continuous learning.

  • AstraZeneca believes in the transformative power of data and AI, applying them in multiple verticals to drive breakthroughs in patient care.

  • With cutting-edge infrastructure and a dynamic work environment, AstraZeneca is helping define the future of healthcare.

  • The Bengaluru office serves as a key tech and analytics hub for AstraZeneca's global operations.

  • Employees have access to advanced tools and leadership support to create impactful solutions across the business.


Profile Overview:

  • This role is ideal for individuals who enjoy solving real-world business and scientific problems through data.

  • The selected candidate will be responsible for using data science techniques such as machine learning, statistical modeling, and NLP.

  • You will be collaborating with cross-functional teams to build and deliver solutions that impact scientific research and business decisions.

  • You will interpret data, identify trends, and generate meaningful insights for high-impact projects.

  • The role involves working on varied data sources including structured, unstructured, and multimodal data.

  • You’ll contribute to new AI-driven strategies in drug discovery, clinical trials, and commercial functions.

  • Your expertise will support decision-making by creating reproducible, high-quality models and algorithms.

  • You’ll also contribute by coaching team members and enhancing data science capabilities across teams.


Qualifications:

  • A Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a closely related field is required.

  • Strong grasp of statistics, probability, and data modeling principles.

  • Demonstrated ability to work with real-world datasets and build ML models from scratch.

  • Proficiency in Python, with hands-on knowledge of Scikit-learn, TensorFlow, or similar libraries.

  • Experience working with data visualization platforms such as Tableau or Power BI.

  • Familiarity with big data environments like Hadoop or Spark is a plus.

  • Should be comfortable working in a team and also taking ownership of individual deliverables.

  • Effective communication and storytelling ability with stakeholders is essential.


Additional Info:

  • This is a high-impact role offering the chance to work on projects that influence strategic decisions.

  • The company promotes in-person collaboration for fast-paced and high-quality output but remains flexible for individual circumstances.

  • You’ll be exposed to data science applications in the fields of genomics, clinical development, and commercial operations.

  • There is strong encouragement for publishing work, attending conferences, and building visibility in the data science community.

  • The team consists of experienced data professionals who foster a supportive and learning-centric culture.

  • You’ll get access to state-of-the-art tools, cloud-based platforms, and continuous learning opportunities.

  • AstraZeneca values personal growth and offers career development opportunities through internal mobility and learning resources.

  • You’ll be making a real-world impact through your work while being supported by global leaders in science and technology.


Roles and Responsibilities:

Data Handling and Analysis

  • Work on a variety of complex datasets using statistical analysis, ML models, and pattern recognition techniques.

  • Design and implement end-to-end data pipelines including preprocessing, feature engineering, and validation.

  • Integrate structured and unstructured data from internal systems and external sources.

  • Convert business needs into actionable data science problems and propose suitable solutions.

Model Development and Deployment

  • Build and deploy machine learning models including regression, classification, and clustering techniques.

  • Apply natural language processing, computer vision, graph theory, and other advanced AI concepts as needed.

  • Ensure all models are tested, validated, and deployed in production-grade environments.

  • Use cloud-based platforms and open-source libraries to build scalable solutions.

Collaboration and Communication

  • Partner with stakeholders to understand data requirements and translate them into technical implementations.

  • Collaborate with software engineers, product owners, and business leaders to ensure alignment.

  • Present insights and solutions in an accessible format for non-technical stakeholders.

  • Communicate uncertainties, assumptions, and caveats clearly in reports and presentations.

Research and Innovation

  • Conduct research to enhance existing models or develop new ones for better performance and scalability.

  • Stay up to date with advancements in machine learning, AI, and data science tools.

  • Explore novel algorithms that fit unique business challenges where standard solutions fall short.

  • Contribute to knowledge-sharing sessions and internal data science forums.

Training and Mentoring

  • Offer guidance to junior data scientists and analysts.

  • Provide internal training sessions on topics such as model interpretation, best practices, and tool usage.

  • Act as a go-to resource for key data science techniques and platforms within the team.


Please click here to apply.


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