Nift

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We're hiring a hands-on Senior Applied Data Scientist to help build and scale production-grade recommendation systems that drive our core marketplace outcomes. This is not a research-only role. We're looking for someone who can take models from idea to production — running experiments, measuring business impact, and continuously improving the systems behind how Nift matches people with the right brands. You'll own the full lifecycle: exploratory analysis, data prep, modeling, testing, deployment, and post-launch measurement. The ideal candidate has worked in a real production environment, brings strong deep learning experience, and understands recommendation systems in practice — not just in theory. Success here means shipping models that move Nift's core KPIs, connecting technical work to measurable business impact, and helping the team scale with strong engineering discipline. This role is ideally based in Israel, but strong candidates in the U.S. will also be considered. What You'll Do • Own the full funnel of applied machine learning work, from idea through production • Build, improve, and deploy recommendation models that support Nift's core business goals • Tackle deep learning problems in a production setting — not just offline experimentation • Conduct exploratory data analysis, preprocessing, feature development, and modeling • Run experiments and evaluate success against business KPIs, not just model metrics • Partner with engineering and infrastructure teammates to productionize models and scale systems • Improve recommendation quality, personalization, and the business performance tied to those systems What You'll Have • 5+ years of experience in production data science environments • Strong hands-on experience taking machine learning models into production • Strong deep learning experience; proficiency with PyTorch or TensorFlow is expected • Direct experience with recommendation systems, or adjacent experience in areas like bidding or dynamic pricing • Strong Python and SQL skills • Experience working with data at meaningful scale — high-scale environments are a strong plus • The ability to measure model success through business outcomes such as revenue, conversion, churn, or similar KPIs Bonus points for: • A Master's degree, especially paired with strong production experience • A PhD paired with meaningful production-grade work (purely academic backgrounds aren't the target profile for this role) • A software engineering background — particularly for candidates who've built pipelines and production systems before moving into machine learning About Us Our mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded. We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now we're scaling to become one of the largest sources of new customer acquisition worldwide. Backed by investors who supported Fitbit, Warby Parker, and Twitter, we're poised for exponential growth and ready to demonstrate impact on a global scale.

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re looking for a hands-on ML Ops Engineer to partner with our data scientists to turn their models into production-ready systems. In this role, you’ll report to the Data Science Manager and work closely with our Data Scientists and Product developers. You’ll architect storage and compute, harden training/inference pipelines, and make our ML code, data workflows, and services reliable, reproducible, observable, and cost-efficient. You’ll also set best practices and help scale our platform as Nift grows. Our Mission: Nift’s mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded. We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now, we’re scaling to become one of the largest sources for new customer acquisition worldwide. Backed by Spark Capital & Foundry who also invested in Slack, Snap, SeatGeek, Fitbit, Warby Parker, Wayfair and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growth here. What you will do: • ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governance • Feature & model lifecycle: Centralize feature generation (e.g., feature store patterns), manage model registry/metadata, and streamline deployment workflows • Observability & quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboards • Engineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility • Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance • Roadmap & standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standards What you need: • Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systems • Software engineering strength: Strong coding, debugging, performance analysis, testing, and CI/CD discipline; reproducible builds. Extensive commercial experience with Python developing automated pipelines bringing ML models to production • Cloud & containers: Production experience on AWS, DataBricks, Docker + Kubernetes (EKS/ECS or equivalent) • IaC: Terraform or CloudFormation for managed, reviewable environments • ML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelines • Monitoring/observability: ML monitoring (quality, drift, performance) and pipeline alerting • Collaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startup • PySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processing • Analytics platforms: Experience integrating 3rd party data • Model serving patterns: Familiarity with real-time endpoints, batch scoring, and feature stores • Governance & security: Exposure to model governance/compliance and secure ML operations • Be mission-oriented: Proactive and self-driven with a strong sense of initiative; takes ownership, goes beyond expectations, and does what's needed to get the job done What you get: • Competitive compensation, flexible remote work • Unlimited Responsible PTO • Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success

Nift is at a pivotal stage of growth. We are disrupting performance marketing by delivering millions of new customers to brands every month, and we have the data to prove it—now we need the insight to guide it. We are looking for a Data Analyst to serve as the analytical bridge between our marketplace economics and daily business operations. This is not a "ticket-pulling" role; this is a strategic partnership role. You will wear two hats: acting as the dedicated, day-to-day data partner for our Account Managers, and operating as a proactive business intelligence engine to uncover where our next growth opportunities lie. You will be the person with your ear to the ground, taking large-scale marketplace data and turning it into simple, meaningful actions that shape our product and business decisions. If you are an AI-native problem solver who loves automating the "boring" work to focus on high-impact strategy, we want to talk to you. What You'll Do • Be the Business Bridge: Act as the primary data point-of-contact for Account Managers. You’ll help them diagnose complex performance issues, explain why things are happening in the data, and suggest clear, actionable next steps. • Proactive Business Intelligence: Look beyond the dashboard. Identify patterns in supply, demand, and brand performance. You’ll surface the findings—like user history modeling or cohort analysis—that influence company strategy every few weeks. • Automate & Scale: We don’t just want you to run queries; we want you to build. Whether it’s automating classification, optimizing supply/demand tagging, or building models that make our data more accessible to the team, you’ll focus on building for the long term. • AI-Native Automation: A massive green flag for us is someone who builds bots (Slack, email, or web apps) to share knowledge and automate repetitive workflows. You’ll help us build a smarter, more efficient data culture. What You'll Have • 5+ Years of Experience: You’ve spent significant time working with data in a professional environment and understand how to handle data at scale. • SQL (Must): You are an expert. You can navigate complex datasets to find the answer quickly and accurately. • Recommendation System Familiarity (Must): You understand the vocabulary (e.g., selection bias, cold start, feedback loops). You need to know the context of how these systems work to speak confidently with stakeholders. • Business Intuition: You have a math or statistics background (bonus), which helps you reduce false claims and provide the business with reliable, defensible insights. • Technical Versatility: Python is a major bonus, especially if you use it to automate work or build lightweight tools. • Communication: You know how to talk to non-technical partners (Account Managers, clients) and translate complex data into a narrative that drives action.Is About Us Our mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded. We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now we're scaling to become one of the largest sources of new customer acquisition worldwide. Backed by investors who supported Fitbit, Warby Parker, and Twitter, we're poised for exponential growth and ready to demonstrate impact on a global scale.