About Liberis

Liberis was founded and is backed by Blenheim Chalcot.

Liberis is on a mission to supercharge the power of small businesses around the world by delivering the financial products they need to grow, through our global network of partners. Before all else, Liberis is a technology company, connecting finance with small businesses. We use data to help partners understand their customers’ real-time needs and build technology that enables partners to offer tailored funding and financial products. To date, we’ve funded almost 50,000 small businesses with over $2bn - but we believe this is just the beginning. 

The Team

We are the Risk team responsible for managing the credit risk of Liberis’ portfolios, which include designing effective new strategies, managing the credit quality of our products across geographies, and providing specialist credit risk advice to the business.

The Risk team is based in London and covers Risk analytics, Decision Science, Underwriting and Collections. Our mission is to grow Liberis profitably to become the leading embedded business finance provider and we are now looking for a Analyst/Senior Credit Risk Analyst to join us on this journey.

The Role

You are someone that is excited by the prospect of a challenge, appreciate autonomy and the space to generate your own ideas and are passionate about finance that serves to support small businesses, not just turn a profit.

Reporting to the Senior Manager in the Risk Management team you will develop best in class credit risk strategies that reward our members and deliver value to Liberis. You will have the opportunity to work on a broad range of projects and workstreams that provide credit risk insights while ensuring we stay within the organisation’s risk appetite.

You should have experience in a similar analytical role where you have used statistical and analytical tools to drive innovative strategy changes. You should be able to derive insights from data and be able to present results in a concise way to relevant stakeholders.

What you’ll be doing

1. Risk Data Architecture & Structuring

• Design and maintain scalable credit risk data marts and portfolio datasets.
• Define and document data definitions, schemas, and maintain metric logic for key risk indicators (e.g. delinquency, roll rates, vintage curves, loss metrics).
• Partner with Data Engineering to optimise warehouse structures (Snowflake/BigQuery/Redshift or equivalent) and to improve the accuracy, consistency, and automation of risk reporting processes.
• Ensure consistency and governance across risk, finance, and commercial datasets.
• Validate and reconcile underlying data sources, ensuring data quality standards are upheld.
• Use data to ensure the execution of our strategy is in line with expectations with the right governance, control, and monitoring.

2. Building scalable Reporting & Automation layer

• Build reusable reporting layers that scale as origination volumes and product complexity increase.
• Create self-serve reporting environments for risk stakeholders while maintaining control frameworks.
• Prepare reporting and datasets for new initiatives on an ad-hoc basis and deep-dive insights required by Risk, Finance, and executive and senior leadership.
• Create ability to do data mining by the rest of the risk team.
• Bring visibility to more datasets that will support risk team’s day-to-day analytics and operational management towards SLAs

3. Supporting Portfolio Monitoring & Analytical Frameworks (Build scalable monitoring frameworks covering as below)

• Portfolio performance tracking
• Early warning indicators
• Concentration and exposure monitoring
• Vintage and cohort analysis
• Stress and sensitivity views
• Ensure reporting can flex across products, segments, and funding structures.

4. Ensuring Data Quality, Controls & Governance

• Implement validation checks and automated reconciliations within reporting pipelines.
• Own data integrity across risk reporting outputs.
• Develop monitoring for data drift and structural anomalies.
• Support internal audit and regulatory reviews with well-documented reporting logic and traceability.
• Closely collaborate with colleagues across Liberis, especially in the Engineering, Analytics, Product, Growth, Partnerships, and UK/US/European teams.

What we think you’ll need

• Bachelor’s or Master’s degree in Finance, Economics, Statistics, Mathematics, Data Analytics, Engineering or related quantitative discipline.
• 2-4 years experience in data management, building data marts or database maintenance.
• Strong affinity to build deep understanding of data flows and build re-usable datasets for the end-analytical users. Experience in a similar data role is a must.
• Demonstrated ability to drive high-quality analysis, with a robust understanding of how this will be used practically.
• Advance level proficiency and strong experience with databases management and analytical data product creation; Minimum 1 year of experience with Google Cloud Platform tools such as Looker, DBT, and BQ would be a must-have (minimum 6-months exp)
• Intermediate level proficiency in Excel and at least one BI/data visualisation tool (e.g.,Power BI, Tableau, Looker).
• Advanced SQL proficiency - ability to write complex queries independently across large datasets
• Ability to convey complex findings in a logical and easy-to-understand style.
• Turns rigorous analysis into clear, reasoned recommendations.
• Excellent analytical and problem-solving skills with strong attention to detail (must-have)
• Clear communicator with ability to explain complex data insights to non-technical stakeholders.
• Strong judgement, with the ability to make trade-offs between what is desired and what is feasible.
• Comfortable working in a fast-moving, ambiguous, and changing company.
• Experience working cross-functionally with engineering, risk, operations, and finance teams.
• A collaborative mindset and willingness to work with a wide range of teams.
  Experience in lending analytics or fintech credit products.
• Understanding of credit risk metrics and frameworks (PD/LGD/EAD) and lending

Liberis is an equal opportunities employer. We welcome applications from all candidates, including individuals with disabilities and provide reasonable adjustments as required.

Strategic Project Lead / Delivery Manager 

Data Annotation & AI Delivery

8+ Years of Experience

Position Overview

We are looking for a seasoned Delivery Manager (8+ years of experience) to lead large-scale data annotation and curation projects in support of cutting-edge LLM and multimodal AI research. You will oversee end-to-end delivery for projects with 100+ team members, ensure world-class data quality, and drive process and tooling innovation in collaboration with AI researchers and cross-functional teams.

This role is ideal for someone who combines deep technical expertise, proven leadership in managing large teams, and a passion for advancing AI/ML innovation. Given that our key customers operate in the data analytics, data engineering, and Business Intelligence (BI) space, the ideal candidate will bring hands-on leadership experience with platforms such as Databricks and Snowflake, enabling them to deeply understand customer domains, speak their language, and deliver annotation outcomes that are contextually relevant and technically sound.

Primary Responsibilities

1. Team Leadership & Development

• Lead, mentor, and grow large-scale teams of annotators and trainers.
• Drive performance management, career development, and targeted training.
• Foster a culture of excellence, ownership, and continuous learning.
• Design and implement motivation strategies (including gamification) to boost engagement and reward top performers.

1. Project Management & Coordination

• Define and manage project scope, objectives, timelines, and quality metrics.
• Align with cross-functional stakeholders on goals and deliverables.
• Proactively identify and resolve delivery risks, blockers, and dependencies.
• Provide clear and consistent reporting on progress, challenges, and solutions.

1. Data Quality & Integrity

• Own quality control and data integrity across the annotation lifecycle.
• Analyze datasets to identify trends, anomalies, and insights for improvement.
• Implement best practices for annotation, evaluation, and data curation.
• Share learnings and continuously improve quality processes.

1. Technical Collaboration & Innovation

• Partner with engineering and research teams to optimize internal tooling.
• Stay ahead of emerging trends in AI, ML, and multimodal processing.
• Champion innovations that reduce task completion times and improve efficiency.
• Maintain clear and scalable documentation for processes and training.

1. Customer & Domain Engagement

• Serve as a domain-knowledgeable point of contact for customers operating in data engineering, analytics, and BI environments.
• Leverage hands-on experience with platforms like Databricks and Snowflake to understand customer data workflows, terminology, and quality expectations.
• Translate customer-specific data engineering and BI contexts into clear, actionable annotation guidelines and quality benchmarks for delivery teams.
• Collaborate with customers to define annotation taxonomies and evaluation criteria that align with their data pipeline and analytics use cases.

Required Skills & Qualifications

• 10+ years of experience in engineering, program management, or delivery of large-scale projects.
• Proven ability to lead and scale large teams (100+ members) with measurable impact.
• Strong technical grounding in AI/ML, data annotation, and multimodal systems.
• Hands-on leadership experience in data analytics, data engineering, or BI domains, with working knowledge of platforms such as Databricks and/or Snowflake (architecture, pipelines, data modeling, and production workflows).
• Familiarity with the modern data stack including ETL/ELT pipelines, data warehouses, data lakes, and BI tools (Power BI, Tableau, Looker, or similar).
• Ability to translate complex data engineering and analytics concepts into annotation frameworks, quality standards, and training materials.
• Proficiency in Python (preferred) or Swift for data analysis and automation.
• Strong mathematical and analytical skills for data-driven problem solving.
• Experience in data curation, annotation pipelines, and quality control.
• Excellent communication and stakeholder management skills.
• Track record of driving high-quality outputs under tight deadlines.

Preferred Skills & Qualifications

• Advanced degree in Computer Science, Data Science, AI, or related fields.
• Experience applying gamification to workforce engagement.
• Familiarity with modern project management tools & methodologies.
• Exposure to state-of-the-art multimodal AI technologies.
• Prior experience managing annotation or data labeling projects for customers in the data engineering, analytics, or BI space.
• Familiarity with SQL at an advanced level and data orchestration tools such as Apache Airflow, dbt, or Prefect.
• Certifications in Databricks (e.g., Data Engineer Associate) or Snowflake (e.g., SnowPro Core) are a plus.

We are building PIE (Prodigal Intelligence Engine) - the specialized intelligence layer that transforms AI agents from conversational interfaces into truly intelligent decision-makers. We're building the brain that makes agents understand context, make compliant decisions, and deliver personalized experiences at scale. Read more about PIE here

About the role

As a Senior Software Engineer on the Platform team, you'll architect the systems that power PIE - unifying siloed data sources across CRMs, lending systems, dialers, and communication channels into a unified intelligence layer. You're building infrastructure that doesn't just handle data at scale, but extracts meaning from it, enabling AI agents to make contextually intelligent, compliant decisions in real-time.

This isn't incremental improvement - it's foundational work toward an agentic future where specialized AI agents collaborate autonomously to manage entire collections operations. The systems you build today become the backbone of tomorrow's agentic workforce.

What you'll do

• Design and build the distributed systems infrastructure that powers PIE, processing millions of consumer interactions daily while maintaining sub-100ms response times for real-time agent decision-making
• Build robust data unification pipelines that reconcile and synthesize information across heterogeneous systems (CRMs, LMS platforms, dialers, communication channels) into coherent, actionable intelligence
• Create scalable platforms that can ingest, process, and serve insights from 500+ consumer attributes while supporting complex regulatory and compliance requirements
• Own end-to-end architecture and implementation - from high-level system design through production deployment and operational excellence
• Write production-grade code in Python, Golang, and Node.js that balances performance, maintainability, and speed of iteration
• Conduct rigorous code reviews, tackle complex distributed systems bugs, and take ownership of high-risk technical challenges
• Build comprehensive testing strategies spanning unit, integration, and regression capabilities to ensure reliability at scale
• Implement sophisticated monitoring, observability, and troubleshooting capabilities for complex distributed systems
• Drive improvements in key technical metrics: system availability, data freshness, query performance, and cost efficiency
• Partner with ML teams to build infrastructure that supports model training, inference, and continuous learning loops
• Influence technical direction and architectural decisions that span multiple teams and projects
• Mentor junior engineers through code reviews, architectural discussions, and collaborative problem-solving
• Champion engineering best practices, documentation standards, and operational excellence across the platform team

✅ Requirements

• B.Tech./BE/M.Tech Equivalent in Computer Science or relevant education
• 4+ years architecting and building high-throughput, distributed data platforms that process millions of events daily
• Strong hands-on expertise with NoSQL & SQL databases (MongoDB, DynamoDB, PostgreSQL) and modern data warehouses (Redshift, Snowflake, Databricks BigQuery)
• Deep experience building scalable, secure, highly available software that operates reliably under production load
• Proficiency with AWS services, containerization (Docker/Kubernetes), and modern DevOps practices
• Expertise in observability and monitoring tools (Sentry, CloudWatch, Prometheus, ELK) for troubleshooting production systems
• Strong expertise in troubleshooting complex production issues.
• Good problem solving, critical thinking, and communication skills. Should be able to find the right balance between perfection and speed of execution
• Self-starter and self-learner, who never settles
• Comfortable working in a fast paced environment while continuously evaluating emerging technologies
• Bonus: Foundational knowledge of Machine Learning and AI systems - understanding how models are trained, served, and integrated into production applications
• Bonus: Experience building platforms that power real-time AI/ML workloads or agentic systems

⚙️ Our Tech Stack

• Languages & Frameworks: Python, Golang, Node.js
• Databases: MongoDB, PostgreSQL, Databricks
• Infrastructure: AWS (EKS, EC2, Lambda), Kubernetes, Docker
• GenAI Tooling: Cursor, Claude code, and all other leading AI dev tools 

Mode of Work - In-Office (Powai, Mumbai)

What we offer

• GenAI experience – Work directly in the innovative field of GenAI, shaping groundbreaking projects that redefine consumer finance intelligence.
• World-class team - You'll get the chance to learn from (and teach) some of the brightest and most skilled people you'll ever meet. Our team members have been part of BCG, Deloitte, EY, Blackstone, Meta, Amazon and are IIT, IIM, BITS alumnus.
• Continuous education - As relentless seekers of knowledge, we sponsor and support any training materials, books, courses, and exam fees for upskilling yourself in areas related to your role - but that's not where it ends. If you make a good case for some extra learning, we're delighted to foot the tab.
• Food at the office – Meals are on us. We won't let your stomach grumble while you hustle.
• Health insurance - Health always comes first. The health of your family is as important to us, as it is to you. We offer insurance coverage for both you and your family.
• Flexible schedule - We're not a "clock in, clock out" company. Morning person? Start work while the world's still sleeping. Night owl? Ramp things up while others are winding down. You're fully trusted to create the right conditions for your own personal peak productivity. We only ask that you be available to your teammates for seamless collaboration.
• Generous leave policy - Take all the time you need to recharge your batteries - no cap on vacation here. Because a rested, relaxed, and refreshed mind is a happy and effective mind.
• Recreation & team activities – We regularly indulge in friendly football, table tennis, cooking sessions and challenge each other to do those extra push ups or chug that 4th drink (of course that’s Nimbu Pani).