Data Scientist | Shop
About Addi
We are a leading financial platform, building the future of payments, shopping, and banking—a world where consumers and merchants can transact effortlessly and grow together. Today, we serve over 2 million customers and partner with more than 20,000 merchants, making Addi Colombia’s fastest-growing marketplace.
With a state-of-the-art, technology-first approach, we provide banking solutions (deposits, payments, unsecured credit) and commerce services (e-commerce, marketing), bridging the financial gap for millions and redefining how people experience financial freedom. As the country’s leading Buy Now, Pay Later provider, we have secured regulatory approval to operate as a bank, unlocking even greater opportunities for our customers. In the past year, we have also achieved profitability, reinforcing the strength of our business model and our ability to scale sustainably.
Our mission has earned the trust of world-class investors, including Andreessen Horowitz, Architect Capital, GIC, Goldman Sachs, Greycroft, Monashees, Notable Capital, Quona Capital, Union Square Ventures, Victory Park Capital, and more, who back our vision for the future. With their support, we are not just growing—we are transforming Latin America’s financial ecosystem and shaping the next generation to shop, pay, and bank in Colombia.
But what truly sets us apart is how we build. We are a conscious company, driven by deep experience in scaling technology, services and products, and we live by our values every day.
About the Role
This is where you come in. Below, you’ll find what this role is all about—the impact you’ll drive, the challenges you’ll tackle, and what it takes to thrive at Addi. If you’re ready to be part of something big, keep reading.
What’s the mission you’ll drive
Design, build, and operate the Decision Intelligence Engines that power Addi’s personalized customer journeys, while transforming Addi’s Shop into an automated, AI-driven ecosystem by deploying State-of-the-Art (SOTA) architectures, including Sequential Deep Learning and LLMs to optimize customer LTV, activation, and retention in real-time.
What you will do
Segmentation & Behavioral Analysis: Design and maintain segmentation models based on behavior, performance, lifecycle stage, and growth potential.
Outcome Prediction: Design, train, and deploy models to predict customer behaviors and risks, ensuring outputs are interpretable and segment-aware.
Applied AI Production: Design and deploy LLM-based solutions for customer growth, treating them as production systems with strong guardrails.
Develop and Implement ML Models: Design, implement, and scale machine learning and ML models to analyze customer behavior, optimize marketing strategies, and improve overall engagement with Addi’s platform. This includes leveraging techniques like supervised and unsupervised learning, propensity scoring, and recommendation systems.
Manage Data Pipelines and Model Deployment: Collaborate with data engineering teams to design and optimize data pipelines that support the seamless deployment of the models into production. Ensure that models are integrated efficiently and can be scaled, maintained, and monitored for performance in a live environment.
Monitor and Evaluate Model Performance: Continuously monitor the performance of deployed models, evaluate their impact on business metrics, and iterate to improve their accuracy, scalability, and overall performance.
Collaboration and Knowledge Sharing: Work closely with product managers, marketing teams, and stakeholders to translate data insights into actionable strategies, and actively participate in cross-functional meetings to align ML models with business goals.
Innovate and Improve Processes: Continuously innovate by proposing ML models, algorithms, or tools that enhance customer experience, optimize product recommendations, and improve overall marketplace performance.
Conduct A/B Testing: Design and execute A/B tests to assess the impact of different offers, product recommendations, and marketing strategies on customer engagement and conversion rates. Analyze the results to understand customer sensitivity to various factors and refine approaches accordingly.
What we’re looking for
Proven Track Record in Data Science & Recommender Systems
Bachelor’s or Advanced degree in a quantitative field such as Mathematics, Statistics, Physics, Economics, or Computer Science.
4+ years of professional experience in Data Science roles with a primary focus on Recommender Systems, Growth, or Marketplace dynamics.
Deep theoretical understanding of the first principles behind machine learning algorithms, enabling the implementation of SOTA architectures (e.g., Sequential Deep Learning).
History of building and scaling personalization engines that successfully moved core business KPIs such as GMV, activation, and retention.
Direct experience navigating the complexities of two-sided marketplaces or high-volume consumer apps where supply/demand balancing is critical.
Specific success in deploying models that improve user discovery or conversion within a digital storefront or "Shop" environment.
Proven Track Record in Recommender Systems & Growth
Ability to architect and scale personalization engines that move beyond simple heuristics to drive core business KPIs like GMV and retention.
Deep understanding of marketplace dynamics, specifically how to balance supply (merchants) and demand (customers) within a digital shop.
Demonstrates Full-Stack Ownership of the ML Lifecycle
Capacity to lead a project from initial problem framing and stakeholder alignment to production deployment and proactive monitoring.
Ensures models are not just mathematically sound but are robust, scalable, and operationally reliable in a live production environment.
Has Solid Expertise in Decision Intelligence & NBA
Skilled in leveraging Causal Inference and Reinforcement Learning to transition ecosystems from rule-based logic to proactive, probabilistic decision-making.
Experienced in building Next Best Action (NBA) models that optimize the customer journey in real-time.
Experienced in State-of-the-Art Modeling (LLMs)
Hands-on experience fine-tuning and deploying Large Language Models (e.g., Qwen, Llama) within real-world business workflows.
Ability to integrate LLMs into hybrid architectures to solve complex problems like catalog enrichment and automated customer insights.
Possesses Advanced Engineering & AI Orchestration Skills
Mastery of Python, SQL, and PySpark for large-scale data processing and model training.
Proficiency in deep learning frameworks (PyTorch/TensorFlow) and modern AI orchestration tools like LangChain to build agentic systems.
Track Record of Rigorous Experimental Design
Proven ability to design and analyze A/B tests while accounting for segment-level heterogeneity and selection bias.
Expert at defining success metrics and building frameworks for offline evaluation and online KPI tracking to reduce regressions.
Displays a Segment-Aware Growth Mindset
Operates with the fundamental understanding that different user and merchant segments require distinct strategies; avoids "one-size-fits-all" solutions.
Constantly looks for "high-leverage" opportunities within data to unlock non-linear growth for the platform.
Why join us?
Work on a problem that truly matters – We are redefining how people shop, pay, and bank in Colombia, breaking down financial barriers and empowering millions. Your work will directly impact customers' lives by creating more accessible, seamless, and fair financial services.
Be part of something big from the ground up – This is your chance to help shape a company, influencing everything from our technology and strategy to our culture and values. You won’t just be an employee—you’ll be an owner
Unparalleled growth opportunity – The market we’re tackling is massive, and we’re growing faster than almost any fintech lender at our stage. If you’re looking for a high-impact role in a company that’s scaling fast, this is it.
Join a world-class team – Work alongside top-tier talent from around the world, in an environment where excellence, ownership, and collaboration are at the core of everything we do. We care deeply about what we build and how we build it—and we want you to be a part of it.
Competitive compensation & meaningful ownership – We believe in rewarding our talent. You’ll receive a generous salary, equity in the company, and benefits that go beyond the basics to support your growth.
How the hiring process looks like
We believe in a fast, transparent, and engaging hiring experience that allows both you and us to determine if there's a great fit. Here’s what our process looks like:
Step 1: People Interview (30 min)
A conversation with a recruiter or hiring manager to get to know you, your experience, and what you're looking for. We’ll also share more about Addi, our culture, and the role.Step 2: Initial Interview (45-60 min)
A more in-depth conversation with the hiring manager, where we explore your skills, experience, and problem-solving approach. We want to understand how you think and work.Step 3: Take Home Challenge (5-6 days)
Complete a simple take-home challenge within a 1-week window. With this technical challenge, we want to see your technical expertise solving a real-world problem. We expect that you invest 5 hours or less in developing a working solution.Step 4: Take Home Challenge Review (60 min)
Meet with a Data Scientists and the Data Science Lead to talk about your take-home exercise submission and any questions you might have.Step 5: Co-Founder Interview
If there’s a strong match, you’ll have a final conversation with our Founder to align on expectations, cultural fit and ensure mutual excitement. From there, we’ll move quickly to an offer and discuss next steps.
We value efficiency and respect for your time, so we aim to complete the process as quickly as possible. Our goal is to make this experience insightful and exciting for you, just as much as it is for us. Regardless of the outcome, we are committed to always providing feedback, ensuring that you walk away with valuable insights from your experience with us.