Analytical Engineering Review

During my industrial placement year at Lloyds Banking Group, I worked as a Data Scientist within the EconMetrics team. My role primarily focused on using data-driven methods to support strategic decision-making through statistical modelling, exploratory analysis, and performance evaluation. I contributed to several projects that combined technical rigour with business insight, ensuring that analytical outputs were interpretable and actionable for senior stakeholders.
Day-to-Day Responsibilities:
Data Preparation and Cleaning: Extracted, transformed, and cleaned large datasets using Python and SQL to ensure quality and consistency for analysis.
Statistical and Machine Learning Analysis: Built and validated models to understand customer behaviour, evaluate performance metrics, and identify key drivers of satisfaction and engagement.
Exploratory Data Analysis: Created data visualisations and density plots to uncover trends, outliers, and relationships between key variables.
Performance Tracking: Conducted comparative analyses of main and exclusive bankers using updated 2024 satisfaction data, applying statistical methods such as distribution analysis and non-overlapping population comparisons.
Collaboration and Reporting: Worked with data engineers, business analysts, and senior data scientists to refine insights and present results clearly in dashboards and presentations.
Continuous Improvement: Contributed ideas to improve the EconMetrics redevelopment framework, streamlining processes for efficiency and scalability.

Yes, I was given a significant level of responsibility throughout my placement. From early on, I was trusted to handle complex datasets and contribute to core analytical projects within the EconMetrics team. I independently carried out statistical analyses, designed visualisations, and presented findings that directly informed decisions about customer satisfaction and engagement.
One of my key responsibilities was conducting a comparative analysis of main and exclusive bankers using updated 2024 satisfaction data — a project that required both technical rigour and business understanding. I was also involved in improving the existing EconMetrics framework, helping make analytical processes more efficient and reproducible.
While I received guidance from my line manager and senior data scientists, I had considerable autonomy in managing my workload, documenting my work, and delivering insights that were later used by internal stakeholders. This balance of support and trust helped me grow technically and professionally.

Yes, I developed several new skills and strengthened many of my existing ones during my placement.
On the technical side, I significantly improved my proficiency in Python, especially in data manipulation, visualisation, and statistical modelling. I also became more confident using SQL for data extraction and transformation, and deepened my understanding of data pipelines, model validation, and performance tracking techniques.
In addition, I learned how to apply mathematical and statistical methods — such as density distributions, hypothesis testing, and correlation analysis — to real-world business data. This helped me bridge the gap between technical analysis and actionable business insights.
Beyond technical skills, I developed strong communication and collaboration skills. Presenting analytical findings to non-technical stakeholders taught me how to translate complex results into clear, meaningful insights. I also grew in time management, problem-solving, and independent project ownership, as I often managed my own analyses from start to finish.
Overall, the placement gave me both the technical depth and the professional confidence to take on more advanced data science challenges in the future.

I received a balanced level of support and guidance throughout my placement. My line manager and senior data scientists were always available to provide guidance on complex tasks, clarify business requirements, and review my analyses to ensure accuracy and clarity. This support helped me quickly understand the team’s processes and expectations.
At the same time, I was given significant autonomy to work independently on projects, manage my workload, and make analytical decisions. This combination of mentorship and independence allowed me to develop confidence in my technical skills, problem-solving abilities, and professional judgement. It also encouraged me to take initiative in improving workflows and contributing ideas to the redevelopment of the EconMetrics framework.

The company culture at Lloyds Banking Group was collaborative, supportive, and professional. There was a strong emphasis on teamwork, knowledge sharing, and continuous learning. Colleagues were approachable and willing to offer guidance, which made it easy to ask questions and seek feedback.
The general atmosphere was both focused and inclusive. While deadlines and project targets were taken seriously, there was also recognition of individual contributions and encouragement to bring forward new ideas. The team valued open communication, and despite being part of a large organisation, I felt my work was meaningful and impactful.
Overall, the culture fostered both professional growth and personal development, making it a positive environment for learning and taking on responsibility.

I really enjoyed my placement at Lloyds Banking Group. The work was challenging, engaging, and highly relevant to my career goals in data science. I found it rewarding to apply my technical skills to real-world business problems and see the impact of my analyses on decision-making processes.
I particularly enjoyed the opportunities for independent project ownership, such as conducting comparative analyses of main and exclusive bankers, while also collaborating with supportive colleagues. The combination of learning new technical skills, improving existing ones, and contributing to meaningful projects made the experience both fulfilling and motivating.
Overall, the placement gave me a strong sense of professional growth and satisfaction, and reinforced my interest in pursuing a career in data science.

Yes