Data Science Intern

Description:

As a Data Science Intern, you will support the development of analytical products that help identify crime hotspots, understand patterns in demand, and provide evidence-based insights to operational and strategic decision-makers. You will also contribute to analytical work supporting Violence Against Women and Girls (VAWG) initiatives, helping to identify locations, emerging risks and opportunities for preventative interventions that can have the greatest impact.

What You'll Be Doing
 

• Analysing large and complex policing datasets to identify trends, patterns and emerging risks.
• Developing hotspot analysis and predictive models to support targeted policing activity.
• Applying statistical and machine learning techniques to real-world operational challenges.
• Producing clear and engaging visualisations to communicate insights to a variety of audiences.
• Supporting the development of analytical products that inform operational and strategic decision-making.
• Working alongside data scientists, analysts and operational policing teams on live business problems.
   

What You Will Gain

This placement offers an excellent opportunity to gain real-world experience in data science within a public-sector environment.

You will work with genuine policing data and develop practical skills in data analysis, predictive modelling, statistics, geographic information systems (GIS), visualisation and evidence-based decision-making. You will gain experience applying analytical techniques to support crime prevention, problem-solving and resource deployment.

The internship will also provide valuable insight into how analytical capability can be used to tackle crime, reduce harm and improve outcomes for communities. You will develop an understanding of how data-driven insights support operational policing and contribute to keeping the public safe.

Who We Are Looking For

The ideal candidate will have:
 

• Strong numerical and analytical skills.
• Experience using Python, SQL, R or similar analytical tools.
• An interest in statistics, machine learning and data visualisation.
• The ability to communicate technical concepts to non-technical audiences.
• Good problem-solving skills and a curious, inquisitive mindset.
• A passion for using data to solve real-world problems.