Data Scientist | Shop

About Addi

We are a leading financial platform building the future of payments shopping and bankinga world where consumers and merchants can transact effortlessly and grow together. Today we serve over 2 million customers and partner with more than 20000 merchants making Addi Colombias 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 countrys leading Buy Now Pay Later provider we have secured regulatory approval to operate as a bank unlocking even greater opportunities for our 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 growingwe are transforming Latin Americas 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 youll find what this role is all aboutthe impact youll drive the challenges youll tackle and what it takes to thrive at Addi. If youre ready to be part of something big keep reading.

Whats the mission youll drive

Design build and operate the Decision Intelligence Engines that power Addis personalized customer journeys while transforming Addis 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 Addis 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 were looking for

• Proven Track Record in Data Science & Recommender Systems

  • Bachelors 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 wont just be an employeeyoull be an owner

• Unparalleled growth opportunity The market were tackling is massive and were growing faster than almost any fintech lender at our stage. If youre looking for a high-impact role in a company thats 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 itand we want you to be a part of it.

• Competitive compensation & meaningful ownership We believe in rewarding our talent. Youll 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 theres a great fit. Heres 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 youre looking for. Well 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 theres a strong match youll have a final conversation with our Founder to align on expectations cultural fit and ensure mutual excitement. From there well 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.

Required Experience:

IC