Data Science

Topsort is the leading Retail Media Infrastructure company - we're democratizing the auction-based ad technology that was once exclusive to Amazon, Google, and Alibaba, and making it accessible to retailers, marketplaces, and delivery apps in 40+ countries worldwide. Our APIs power native sponsored listings, dynamic banners, and clean-room attribution at scale — all privacy-first, all real-time.

We're a fast-growing, global-first team of ~100 people across Boston, Santiago, São Paulo, Barcelona, Sydney, and beyond. We move fast, we build in the open, and we hold a high bar — especially on infrastructure.

Topsort is changing the way Retail Media works. By joining our scaling team, you'll feel your impact from day 1. The processes and messaging you build today will be a critical part of our foundation as we accelerate toward 200 Topsorters worldwide, and your innovations will leave a lasting mark on the industry, as a whole. Retail Media is on the verge of a new era, and Topsort is gathering the ecosystem together to shape a brighter future. Joining us now, you'll work directly alongside our co-founders and founding members to bring on Retail Media 3.0. If you're looking to define your career as something greater than yourself, come scale with us.

Our team is all about straightforward communication, embracing feedback without taking it personally, and fostering a super collaborative environment. It’s a sports team that’s hyper focused on winning, collaborative internally, and competitive externally - never the other way around. 

We thrive on working in the open, lifting each other up, and getting things done with a sense of urgency. We're the kind of team that loves making bold choices, sharing extraordinary opinions, and maintaining a 100 mph pace. No endless meetings here – if it can be done today, we're all about getting it done today.

We're hiring a Data Scientist to help improve how Topsort ranks and retrieves products for search and ads, predicts user behavior, and supports advertiser planning through reliable predictive models.

You'll work closely with Product and Engineering to turn complex data into actionable models and insights that drive better decisions and continuously improve our platform

• Forecasting Models: Develop and refine models for budget planning and advertiser spend, using time-series methods, regression, and machine learning.
• Retrieval: Build and improve the models that select which products are candidates to be shown for each search or placement.
• Ranking: Develop and iterate on rankers and re-rankers that combine relevance, CTR/CVR predictions and business constraints for organic and sponsored placements.
• Query & Product Understanding: Improve how we represent queries and products, including multilingual catalogs, sparse or noisy metadata, and new products with no interaction history (cold start).
• Data Analysis: Explore large datasets to identify trends, assess data quality, and uncover opportunities to improve predictions.
• Production Delivery: Ship models into our ad engine and serving stack, and build the training and evaluation pipelines on large-scale event data.
• Cross-Functional Delivery: Partner with Product and Engineering to integrate models into platform workflows and translate findings into actionable recommendations.

• Reliable Predictions: Models demonstrate consistent performance against defined baselines, offline and in live experiments, with uncertainty and limitations clearly understood.
• Better Relevance: Ranking and retrieval improvements produce measurable lift for shoppers and advertisers.
• Better Planning: Budget and advertiser spend forecasts provide useful inputs for planning and decision-making.
• Practical Adoption: Product and Engineering teams can use model outputs effectively in the platform and relevant workflows.
• Continuous Improvement: Model errors, experiment results and changing data patterns lead to clear, evidence-based improvements.

We’re open to candidates who don’t check every box but show strong potential.

• Relevant Experience: 5+ years of experience in data science, with a strong focus on forecasting.
• Academic Background: A Ph.D. in Data Science, Statistics, Mathematics, or a related quantitative field.
• Ranking & Retrieval: Direct experience building search, recommendation or ads ranking systems in production.
• Deep Learning: Hands-on experience with PyTorch or similar, including training embedding-based retrieval or ranking models.
• Python & SQL: Strong Python and SQL; comfortable building datasets from raw event logs at scale.
• Experimentation: Solid statistics fundamentals and experience designing and reading A/B tests.
• Shipping Mindset: Comfort with AI-assisted development tools and a bias toward getting models into production.

• Retail media / AdTech experience, auction or bidding systems.
• LLM-based retrieval, model distillation, approximate nearest neighbor search (HNSW, FAISS).
• Learning-to-rank at scale, or multi-objective ranking.

Topsort engineering is not for everyone — and that's intentional. You'll do well here if:

• You take ownership seriously . You don't wait to be assigned a problem. If you see something broken, you fix it or escalate immediately.
• You write production-grade code, not proof-of-concepts. You document your work, write runbooks, and leave systems better than you found them.
• You're honest about tradeoffs. We'd rather hear "this will take two weeks to do right" than ship something fragile under the guise of speed.
• You sweat the details — especially in infra. A misconfigured RBAC rule, a missing circuit breaker, an oversized pod resource request — these things matter here.
• You communicate clearly and often. We build in the open. Cross-functional collaboration is expected, not optional.
• You're intellectually curious and you stay current. The infrastructure landscape moves fast. We expect engineers on this team to help us adopt the right new tools — and resist the wrong ones.

We are proud to be an equal opportunity employer and to foster a workplace where diversity is valued and celebrated. We believe that diverse perspectives and experiences strengthen our teams and drive better outcomes. We respect and value any race, religion, color, national origin, ancestry, caste, gender, gender identity or expression, sexual orientation, age, disability, medical condition, pregnancy, genetic information, marital status, or military service.