• Perform independent challenges of machine learning models used for credit underwriting, credit risk, and/or fraud detection through thorough validation and ongoing monitoring • Identify model weaknesses and opportunities for improvement • Collaborate with model owners to remediate model validation findings • Partner cross-functionally to implement and maintain the company’s MRM framework • Partner with Internal Audit, Internal Controls, and Compliance to ensure timely resolution of audit and regulatory requests

• Perform independent challenges of machine learning models used for credit underwriting, credit risk, and/or fraud detection through thorough validation and ongoing monitoring • Identify model weaknesses and opportunities for improvement • Collaborate with model owners to remediate model validation findings • Partner cross-functionally to implement and maintain the company’s MRM framework • Partner with Internal Audit, Internal Controls, and Compliance to ensure timely resolution of audit and regulatory requests

Role Description As a Data Scientist at Jampp, you will help us identify the questions we should be asking and answer them. You will be responsible for tackling challenging problems at scale across many technical disciplines. - Perform ongoing analysis of success metrics to uncover places and ways where our algorithms can be modified to achieve better results. - Refine models that support real-time events and internal stakeholder decisions across the business. - Design, code, and deploy modifications to our algorithms and supporting production code (mostly in Python). - Collaborate daily within a multidisciplinary squad (Devs, Data Engineers, PMs) to ensure data science solutions are integrated into the product strategy. - Communicate and collaborate with the other data science teams. Qualifications - 2-3 years of experience in data science type roles. - Relevant experience in Applied Mathematics, Statistics, Computer Science, Econometrics, or other quantitative field. - Demonstrate proficiency with our current Data Science Stack: Python and Scientific/ML Python stack (numpy, scipy, pandas, mpl, seaborn). - Passion - you have used a data-driven approach to solve a problem you care about. - Highly detail-oriented and exceptional organizational and follow-through skills is a must. - Comfortable conducting daily professional communications in English (written and verbal). Requirements - You may have experience with highly imbalanced datasets or pricing/optimization problems (e.g., Fintech fraud, recommendation engines, or dynamic pricing). - You have working experience implementing complex statistical analyses. - You take pride in working on projects to successful completion, involving a wide variety of technologies, systems, and modeling techniques. - You like working in a self-sufficient, autonomous manner, striving through ambiguity. - You have a sense of urgency and ownership over the product. - Smarts, humility, and equal willingness to learn and teach. Benefits - Learn a ton about the hottest area of growth in Internet advertising - Mobile! - Competitive salary. - Extended Maternity/Paternity leaves. - Extended holidays every year. - A great level of responsibility from day one and the chance to develop your potential without limitations. - An entrepreneurial environment. - A dynamic remote-first work culture with the possibility of accessing coworking spaces as needed. - A structured remote onboarding process and continuous training with a supportive team. We win together! - Work with a talented global team that you can constantly learn from! - Cool swag and team activities (even when working from home!).

• Be part of a high-impact data science team building intelligent logistics systems that optimize delivery operations at a global scale. • Design, develop, and deploy machine learning models and optimization solutions across the full lifecycle — from research and experimentation to production — focusing on planning, forecasting, and operational decision-making. • Apply advanced techniques such as statistical modeling, optimization, geospatial analytics, and forecasting to improve efficiency, reliability, and cost of delivery operations. • Translate complex real-world logistics constraints into scalable mathematical models and data-driven systems. • Contribute to experimentation and performance evaluation through offline analysis and online testing, ensuring solutions are robust, scalable, and aligned with operational goals. • Write production-grade code and build reusable data and modeling pipelines that operate reliably at scale. • Collaborate closely with engineers, product managers, operations teams, and business stakeholders to deliver impactful solutions. • Drive continuous improvement by exploring new methodologies in machine learning, optimization, and applied statistics, raising the technical bar across the organization.