Data Analyst - Python/ML

Octopus Energy Ltd; City of Westminster; Hybrid; GBP 40,000 - 60,000; Full time

Job summary

A leading energy company in the UK is seeking a credit risk analyst to join their team. The ideal candidate will have expertise in SQL and Python, a passion for data analytics, and a strong understanding of customer needs. Responsibilities include managing accounts, developing machine learning models, and creating strategies to help vulnerable customers. Previous experience in consumer credit risk or collections is a bonus. This role is set in a dynamic environment, offering unique opportunities to make a real impact.

Qualifications

• 2+ years of experience in a hands-on role.
• Experience in consumer credit risk or collections in the financial services, utilities, or telecommunications industries is a bonus.

Responsibilities

• Take ownership of management of customers struggling with payments.
• Conduct deep dive investigations into data for insights.
• Develop strategies for vulnerable customers.
• Create fraud prevention strategies.
• Develop and own machine learning models and policies.
• Identify new areas of opportunity.
• Challenge the status quo regarding KPIs and strategies.
• Communicate complex data concepts effectively.
• Build relationships with stakeholders.

Skills

Excellent SQL skills

Excellent Python skills

Familiarity with version control systems

Drive to solve problems using data

Understanding of machine learning and statistics

Python data science stack (pandas, sklearn, numpy)

Tools

Tableau

Looker

PowerBI

dbt

Job description

This could not be a better time to join Octopus Energy. We are already recognised as a global leader in the fight to decarbonise the planet by revolutionising what's possible in energy - including investments in renewable energy supply, renewable energy generation, smart energy networks, EVs, heat pumps, etc. The government's new green initiatives and the recent investment by Al Gore's Generation Fund will propel us further and faster. There has never been a more important moment to join our credit risk team. The energy sector is going through a period of once-in-a-generation volatility. Businesses and households are facing higher energy prices than they ever have before. For these reasons we are looking to add to our credit risk team with this new role. This team sits at the heart of everything we do to support customers that are struggling with their bills. We're unique because we are genuinely a hybrid of a few different skills and mindsets: 1. Data analytics is our core skillset. Everyone in the team is very strong in this area 2. We have a firm understanding of the needs of our customers and the business 3. We work closely with the tech team, because we're a tech company, so this how we solve customer problems, efficiently at scale 4. We work closely with our operations teams who are the people that speak directly to customers

Responsibilities

• Take ownership of our management of customers who are struggling with their payments
• Deep dive investigations into data in order to surface insight for decision making
• Develop our empathic approaches towards vulnerable customers
• Create strategies to identify and prevent first party and third party fraud
• Develop and own our machine learning models & policies that drive sophisticated decisions
• Proactively identify new areas of opportunity
• Challenge the status quo in terms of KPIs, objectives & strategy
• Communicate complex data concepts effectively and confidently
• Build great relationships with Technology, Finance, Collections, Ops and other stakeholders

Core Skills

• Excellent SQL skills
• Excellent Python skills
• Familiarity with version control systems (e.g. git)
• A drive to solve problems using data
• Understanding the basics of machine learning and statistics
• Python data science stack (pandas, sklearn, numpy etc)
• 2+ years of experience in a hands‑on role

What would be a bonus

• Data visualization tool (Tableau, Looker, PowerBI or equivalent)
• dbt
• 2-5 years experience of consumer credit risk or collections in the financial services, utilities or telecommunications industries