Senior Data Scientist

Role & responsibilities

Use a combination of local and cloud tools to analyse, cleanse and transform different types

of data and content for consumption by AI and machine learning models.

• Use the most appropriate data modelling / machine learning principles and tools to design,

explore and build scalable data solutions, potentially from source systems through to the

presentation layer.

• Where required, develop data and software solutions to deliver a robust and automated

deployment of AI/ML services for everyday business use.

• Collaborate with subject matter experts to understand the operational challenges and goals

and to then explore, design and build appropriate data solutions to meet those goals and

alleviate the business challenge.

• Collaborate with software engineers and other IT professionals to understand the

capabilities and constraints around security, data governance and technology infrastructure

to ensure that AI solutions are scalable, automatable, cost efficient and secure and that they

comply with data policies and regulations.

• Work to differentiate ICON from competitors by exploring and developing solutions that

drive meaningful outcomes for customers.

• Monitor and be aware of trends in technology, data sciences and business practice in the

pharmaceutical industry, and provide advice and recommendations on emerging AI/ML

trends and applications.

• Work to deliver maximum customer value for minimum cost.
• Undertake other reasonably related duties as may be assigned from time to time.
• Recognize, exemplify and adhere to ICON's values which centre around our commitment to

people, clients and performance.

• As a member of staff, the employee is expected to embrace and contribute to our culture of

process improvement with a focus on streamlining our processes adding value to our

business and meeting client needs.

Preferred candidate profile

Degree in a relevant IT or natural sciences discipline.

• 6+ years of experience in the use of machine learning, artificial neural networks, natural

language processing and robotic process automation.

• Ability to translate a business problem into an analytical problem that can be solved with

data and machine learning.

• Good knowledge of statistics and statistical analysis.

• Experience with a wide range of data storage technologies and architectures, including data

warehouses, data lakes, graph databases, NoSQL and clustering and experience working

with structured data, semi-structured data and unstructured data.

• Advanced skills in data wrangling, analysis and visualization.

• Mature software engineering skills, including primary code development, code optimization

and workflow and deployment scripting.

• Good knowledge of cloud services, including serverless computing services and machine

learning tools.