Data Scientist

Job Purpose:

The role of the Data Scientist will autonomously identify and pursue research with significant business impact, and make compelling cases for prioritization, resource allocation, and new product strategy · Prioritize and execute in the face of

ambiguity, adapt tools to answer complicated questions and identify the trade-offs between speed and quality.

Key Accountabilities:

Analytics

• Develop analytics policy, standards, and guidelines.
• Establishes and manages analytics methods, techniques, and capabilities to enable the organization to Analyze data, generate insights, create value and
• Drive decision-making.
• Sets direction and leads the introduction and use of analytics to meet overall.
• Business requirements, ensuring consistency across all user groups.
• Identifies and establishes the veracity of external sources of information of relevance to the operational needs of the enterprise.

Methods and tool

• Provides advice, guidance, and expertise to promote the adoption of methods and tools and adherence to policies and standards.
• Evaluate and select appropriate methods and tools in line with agreed policies and standards. Contributes to organizational policies, standards, and guidelines for methods and tools.
• Implements methods and tools at program, project, and team levels including selection and tailoring in line with agreed standards.
• Manages reviews of the benefits and value of methods and tools. Identifies and recommends improvements.

Data management

• Devises and implements master data management processes, including classification, security, quality, ethical principles, retrieval, and retention processes.
  
• Derives data management structures and metadata to support consistency of information retrieval, combination, analysis, pattern recognition, and interpretation, throughout the organization.
• Plans effective data storage, sharing, and publishing within the organization.
• Independently validates external information from multiple sources.
• Assesses issues that might prevent the organization from making maximum use of its information assets.

Data visualization

• Applies a variety of visualization techniques and designs the content and appearance of data visuals.
• Operationalizes and automates activities for efficient and timely production of data visuals. Select appropriate visualization approaches from a range of applicable options.
• Contributes to exploration and experimentation in data visualization.

Data Science

• Applies existing data science techniques to new problems and datasets using specialized programming techniques.
• Selects from existing data sources and prepares data to be used by data science models.
• Evaluate the outcomes and performance of data science models. Identifies and implements opportunities to train and improve models and the data they use.
• Publishes and reports on model outputs to meet customer needs and conform to agreed standards.

Skills

• Excellent interpersonal, verbal, and written communication skills.
• A flexible attitude concerning work assignments and new learning.
• Ability to manage multiple and varied tasks with enthusiasm and prioritize workload with attention to detail.
• Must have the ability to work methodically in a fast-paced, time-sensitive environment.
• Demonstrable ability to apply analytical and critical thinking to problems and tasks.
• Ability to identify and implement process improvements.

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• Proactively participates in skills improvement training and encourages their teams to participate.
• A self-starter and able to work under own initiative.
• Demonstrates effective application of knowledge.
• Proven ability to use Microsoft Office products (including Word, Excel & PowerPoint).

Knowledge and Experience:

• Experience using tools to communicate progress to Stakeholders.
• Experience with programming languages commonly used for data analysis (such as Python, and SQL).
• Experience with Business Intelligence tools (such as Power BI, and Tableau).
• Experience with Relational Databases.
• Experience with Statistical Analysis.
• Understanding of Agile methodologies (preferably Scrum) and UML.
• Awareness of Project Lifecycle and Project Management methodologies Modelling, Process Modelling, and/or other development methodologies.
• Experience in and knowledge of the life sciences sector.
• Solid Professional experience in the same or very similar role.

Education:

• Bachelors Degree in a technical discipline (Math, Science, Engineering, Computing, etc.) or a related study, or equivalent project-related experience.

Note : Candidates from CRO or Pharma background can apply for the role.