Data Scientist
Upload My Resume
Drop here or click to browse · PDF, DOCX, DOC, RTF, TXT
Requirements
• Proven Technical Tenure in Applied AI/ML • 3+ years of experience building and deploying AI/ML solutions end-to-end, specifically in high-impact or internal automation roles. • Evidence of shipping models with guidance in a collaborative environment, moving beyond local notebooks into production systems. • Bachelor’s degree in Physics, Mathematics, Statistics, Economics, or Computer Science, providing the first-principles thinking needed for complex AI debugging. • Possesses Experience with LLM-Based Solutions • Demonstrated success in building or contributing to systems that utilize modern LLM approaches (e.g., LangChain, LangGraph) to solve real-world tasks. • Experience designing knowledge bases or retrieval structures to improve the reliability of AI outputs. • Possesses Strong Classic Data Science Foundations • Demonstrates a deep understanding of statistics, experimentation (A/B testing, sampling), and classical ML methods to ensure AI isn't used where a simpler model suffices. • Proven ability to frame business problems into data science solutions, moving from raw data to production-ready iteration. • Has Solid Expertise in Deep Learning & Transformers • Skilled in neural architectures and optimization, with a working knowledge of attention mechanisms and transformer-based models. • Proficiency in modern frameworks like PyTorch, TensorFlow, or Scikit-learn to build and evaluate models from the ground up. • Experienced in Modern AI & Agentic Systems • Hands-on experience building with LLMs using advanced techniques: prompting, structured outputs, tool use, and guardrails. • Familiarity with orchestration patterns (routing, memory, handoffs) and retrieval-augmented generation (RAG) to build reliable, non-hallucinatory agents. • Demonstrates Ability to Build Production-Adjacent Code • Mastery of Python for creating reproducible pipelines and evaluation tooling. • Comfortable working in shared codebases using Git/GitHub, with the ability to build internal automation utilities and connectors/APIs. • Track Record of Driving Efficiency & Impact • Focused on "Results over Research"—prioritizes automation rate, time saved, and throughput improvements over purely academic model performance. • Takes accountability for the success of projects, ensuring solutions meet both technical stability and business expectations. • Communicates Technical Tradeoffs with Clarity • Exceptional ability to explain the limitations and risks of AI to non-technical stakeholders, ensuring expectations are managed and scope is realistic. • Proactively collaborates with Engineering to navigate production constraints, such as latency, cost, and reliability.
Responsibilities
• Segmentation & Behavioral Analysis: Design and maintain segmentation models based on behavior, performance, lifecycle stage, and growth potential. • Segmentation & Behavioral Analysis: • Outcome Prediction: Design, train, and deploy models to predict customer behaviors and risks, ensuring outputs are interpretable and segment-aware. • Outcome Prediction: • Applied AI Production: Design and deploy LLM-based solutions for customer growth, treating them as production systems with strong guardrails. • Applied AI Production: • 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. • Develop and Implement ML Models • 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. • Manage Data Pipelines and Model Deployment • 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. • Monitor and Evaluate Model 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. • Collaboration and Knowledge Sharing • 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. • Innovate and Improve Processes • 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. • Conduct A/B Testing:
Benefits
• 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. • Work on a problem that truly matters • 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 • Be part of something big from the ground up • 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. • Unparalleled growth opportunity • 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. • Competitive compensation & meaningful ownership • 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 1: People Interview (30 min) • 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 2: Initial Interview (45-60 min) • 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 3: Take Home Challenge (5-6 days) • 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 4: Take Home Challenge Review (60 min) • Step 5: Co-Founder InterviewIf 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. • Step 5: Co-Founder Interview • 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. • always providing feedback