【AstraZeneca】【ComEx】AI Engineering Specialist, Advanced Analytics

■ Job Description / Capsule

The AI Engineering Specialist  will apply algorithmic, data exploration and computational skills to develop AI & data science solutions & capabilities across multiple business areas.  Technical role managing aspects of projects within own specialist area – will have a full understanding of how own function contributes to achieving the objectives of the business. Will use own functional knowledge to deliver high standards, with an increasing freedom to act without direct supervision. The impact is directly related to the quality of solutions developed, the service provided and the degree of influence the role has on others. Will be expected to manage risk in novel situation

■Typical Accountabilities

•    Support our business unit in creating analysis pipeline with defined business issue; architect and create pipeline that meet with defined complex business requirements, maintain and modify existing pipelines to adjust business environment change.
•    Develop and maintain analysis platform which stores vast amount of data to provide machine learning analysis quickly and stably.
•    Be responsible for analysis pipeline development and maintenance by creating pipeline on AWS as a person in charge of insight implementation projects; understand and interpret outcomes generated by advanced modelling, implement pipeline to utilize insights in business operations.
•    Support project to deliver pipeline architecture definition; visualize architecture, contextualize system operation flow, create clear documentations, explain the contents of complex analysis system to non-analytical personnel.
•    Collaborate with various functions (IT, brand team, external vendors etc.) to propose solutions to business unit.
•    Manage operational projects (modify and maintain existing pipelines, expand AI analysis usage etc.) with external vendors.
•    Perform technical expertise in at least one AI specialism (graph recommendation, deep learning, natural language processing, pattern recognition etc.).

■ Education, Qualifications, Skills and Experience

＜Essential＞

•    Bachelors Degree or equivalent number of years of experience in Computer Science or a related quantitative discipline.
•    At least 3 year of project experience of system integration using machine learning models.
•    Constructing operation flow toward BAU.
•    Pipeline building by use of (Py)Spark, Python, SQL.
•    Data extractions by SQL or other related database management languages.
•    AWS services to deploy machine learning models (SageMaker Studio (notebook, pipeline), EMR, Athena, Glue, CI/CD pipeline, ECR).
•    Ability to create documents to clearly explain complex architecture.
•    Business level proficiency in Japanese and English.

＜Desirable＞

•    Masters or PhD in computer science or related quantitative discipline.
•    At least 3 years of experience in pharma industry or consulting farm.
•    Working in pharmaceutical industry or consulting farm.
•    Project management such as data analysis and/or system deployment.
•    Basic machine learning knowledge.
•    Business communication and documentation in both Japanese and English.
•    Experience in life sciences and healthcare.
•    Enthusiasm to gain data-driven digital marketing.
•    Eagerness to absorb new, cutting-edge methods.
•    Good communication and logical thinking skill.

■Career Level

D

■Work Location

Osaka

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.