Merchandise Planner
•Cscgeneration· 11h
🏷️ Data Science⏰ Full-time📈 Mid Level🇺🇸 USA
11h ago
Lendable·about 19 hours ago
Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start:
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)
So far, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance. We get money into our customers’ hands in minutes instead of days.
We’re growing fast, and there’s a lot more to do: we’re 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.
Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1
Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo
Build the best technology in-house, using new data sources, machine learning and AI to make machines do the heavy lifting
We are looking for an Operations Analytics Analyst to join the Operations Analytics team. This is a hands-on role for someone who is strong in Python and SQL, comfortable working with operational data, and excited by the opportunity to build practical analytics and automation solutions.
You will work on a mix of reporting, deep-dive analysis, automation, and product expansion projects. The role is particularly focused on supporting growing products, including US expansion, by setting up reporting baselines, identifying operational inefficiencies, and building proof-of-concept automations that reduce cost-to-serve and improve scalability.
This is a great opportunity for someone who enjoys context-switching between analytical problem-solving, stakeholder management, and hands-on technical delivery.
Build reporting baselines and performance dashboards for new and growing products, including US expansion.
Analyse operational workflows to identify bottlenecks, inefficiencies, and low-hanging 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 prioritise 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.
Minimum 1 year of experience in an analytics, data, operations, or technical role.
Strong Python skills are essential, including experience with data analysis, automation, and working with structured datasets.
Strong SQL skills, with the ability to query, join, transform, and analyse 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.
Experience with dbt or modern analytics engineering workflows.
Experience building or maintaining data pipelines.
Experience integrating with REST APIs.
Exposure to LLMs, prompt engineering, AI automation, or AI engineering workflows.
Experience building end-to-end Python automations or internal tools.
Understanding of operational workflows such as QA, fraud, disputes, AML, IVR, workforce planning, or customer support.
Experience working with product teams or supporting new product launches.
Screening Call + Python Questions
Live Technical Python Interview
Case Study Interview
Final culture-add interviews
Winning team: the opportunity to scale up one of the world’s most successful fintech companies
Flexible working: flexible approach tailored to each role. Hybrid roles require three days in-office weekly; fully remote roles include regular opportunities for in-person connection through socials and off-sites
Socials & connection: opportunities and events to come together, socialise, and get to know each other beyond the office walls
Health coverage: support for your physical and mental wellbeing, including private health cover
Retirement & savings: long-term financial wellbeing through retirement savings plans
Employee referral programme: earn a competitive bonus when you refer successful new team members
Office meals & snacks: enjoy a fully stocked kitchen, plus complimentary lunches prepared by in-house chefs on in-office days at select locations
Sustainable commuting: cycle-to-work and electric vehicle salary sacrifice schemes available in select locations
Please note: The availability and details of specific benefits vary by location and role. For more information, please speak to your Talent Partner.
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