Data Scientist

Role Purpose

Derive business value from data through systematic analysis and interpretation.

Proactively source data from multiple suppliers and conduct advanced statistical and analytical work to extract actionable insights.

Store and manage extracted data in appropriate internal or external environments, expanding database structures as needed.

Engage with clients to communicate strategic results and insights.

Apply advanced analytics technologies, statistical methods, and potentially machine learning and predictive modelling techniques.

Communicate technical findings and methodologies effectively to both technical and business audiences.

Qualifications

Bachelor's Degree in Sciences or Engineering with a strong focus on Computer Science, Statistics, Mathematics, or Actuarial Sciences.

Fluency in Python, SQL, and R.

Beneficial : fluency in C#.Net, C, C++, Visual Basic, or SQL.

Experience

3-5 years of experience in data warehousing, data sciences, and / or modelling environments.

Strong understanding of software languages and software infrastructure.

Proven experience in working with and analysing data.

Insurance industry experience and actuarial background are advantageous.

Outputs

Statistical and Mathematical Skills

Predictive modelling skills.

Knowledge and implementation of machine learning principles.

Critical analytical thinking and attention to detail.

Programming and Technical Skills

High-level proficiency in Python and SQL; experience with R and JavaScript.

Understanding of cloud and web infrastructure.

Experience deploying web applications to Azure (beneficial).

Spark and version control experience.

Data modelling, analysis, and loading from varied formats with awareness of data regulations.

Consuming and validating data from multiple sources.

Managing data version control and ensuring data integrity for auditing.

Geo-spatial analysis.

Using Business Intelligence tools (e.g., Power BI) to present insights.

Business Analysis

Analyse and document processes that translate into deriving business value from data.

Understand the insurance operational environment.

Work with stakeholders to define business requirements for data presentation.

Interpret reinsurance treaties in the context of specific data sets.

Interpersonal Skills

Strong organisational and self-motivation skills.

Ability to work independently and collaboratively across teams.

Understand both technical and non-technical insurance concepts.

Translate business and technical processes into clear documentation.

Competencies

Data Wrangling

Identify and treat imperfections in data (e.g., missing values, inconsistencies).

Ensure data cleanliness and readiness for analysis.

Stakeholder Engagement and Teamwork

Create a collaborative working environment.

Engage effectively with software developers, product managers, and technical specialists.

Data Visualisation and Communication

Communicate analytical findings and techniques to both technical and non-technical audiences.

Self-Awareness and Insight

Build effective relationships, manage ambiguity, and provide perspective in challenging situations.

Software Infrastructure Building

Set up data structures and environments to support analytical processes.

Diversity and Inclusiveness

Engage respectfully and effectively with individuals from diverse backgrounds and cultures.

Data Intuition

Identify and prioritise high-value business problems through data-driven insights.

Independence

Take ownership and accountability for own actions and deliverables.