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About the Role

Lendable is seeking an Operations Analytics Analyst to join their Operations Analytics team. This role requires strong Python and SQL skills, comfort with operational data, and a passion for building practical analytics and automation solutions. You will focus on reporting, deep-dive analysis, automation, and product expansion projects, particularly supporting US expansion by establishing reporting baselines, identifying inefficiencies, and creating automations to reduce costs and improve scalability. This position offers a dynamic environment for individuals who enjoy analytical problem-solving, stakeholder management, and technical delivery.

Responsibilities

Build reporting baselines and performance dashboards for new and growing products, including US expansion.
Analyze operational workflows to identify bottlenecks, inefficiencies, and opportunities for improvement.
Use Python and SQL to investigate operational performance, cost-to-serve, customer outcomes, and commercial impact.
Create proof-of-concept automations using Python, APIs, and LLMs to reduce manual work and improve decision-making.
Support analysis across areas such as QA, disputes, AML, fraud, customer support, vulnerability, workforce planning, and service operations.
Translate ambiguous operational problems into clear analytical questions, outputs, and recommendations.
Work closely with Operations, Product, Data, and senior stakeholders to prioritize and deliver high-impact work.
Support data quality, metric definition, and reporting consistency as new products and processes scale.
Present findings clearly to both technical and non-technical stakeholders.

Requirements

Minimum 1 year of experience in an analytics, data, operations, or technical role.
Strong Python skills, including experience with data analysis, automation, and working with structured datasets.
Strong SQL skills, with the ability to query, join, transform, and analyze large datasets.
Good understanding of basic statistics, including distributions, averages, variance, conversion rates, confidence, and trend analysis.
Basic understanding of data science principles, such as classification, prediction, model evaluation, and feature thinking.
Strong analytical problem-solving skills and the ability to move from problem definition to insight and recommendation.
Comfortable working in ambiguous, fast-paced environments where priorities can change.
Able to operate as both a hands-on analyst and a pseudo-PM when required.
Strong communication skills, with the ability to explain analysis clearly to senior stakeholders.
Comfortable context-switching across reporting, analysis, automation, stakeholder questions, and product support.

Skills

Python
SQL
Data analysis
Automation
Structured datasets
Basic statistics
Data science principles
Analytical problem-solving
Communication
dbt
Modern analytics engineering workflows
Data pipelines
REST APIs
LLMs
Prompt engineering
AI automation
AI engineering workflows
End-to-end Python automations
Internal tools
QA
Fraud
Disputes
AML
IVR
Workforce planning
Customer support

Location

Work Type

Hybrid
Remote

Experience Level

Minimum 1 year of experience

Benefits

Flexible working
Health coverage
Retirement & savings plans
Employee referral programme
Office meals & snacks
Sustainable commuting

About the Company

Lendable is on a mission to build the world's best technology to help people get credit and save money.
Building one of the world’s leading fintech companies.
One of the UK’s newest unicorns with a team of just over 700 people.
Among the fastest-growing tech companies in the UK.
Profitable since 2017.
Backed by top investors including Balderton Capital and Goldman Sachs.
Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot).
Rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance.
Get money into customers’ hands in minutes instead of days.
Going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.