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.
Skills Required
- 5+ years of experience in production data science environments
- Hands-on experience taking machine learning models into production
- Strong deep learning experience (proficiency with PyTorch or TensorFlow)
- Direct experience with recommendation systems or adjacent areas (bidding, dynamic pricing)
- Strong Python and SQL skills
- Experience working with large-scale/high-scale data environments
- Ability to measure model success via business outcomes (revenue, conversion, churn, KPIs)
- Master's degree (preferred)
- PhD with meaningful production-grade work (preferred)
- Software engineering background / building pipelines and production systems (preferred)
Nift Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nift and has not been reviewed or approved by Nift.
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Leave & Time Off Breadth — Generous PTO and paid sick days are highlighted, with work schedules described as highly flexible and well-suited to students. This points to time-off policies that support balance alongside variable hours.
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Retirement Support — A 401(k) plan is offered, indicating baseline retirement planning within the benefits package.
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Equity Value & Accessibility — Total compensation that includes stock or equity is described positively in company culture summaries. This suggests perceived value in equity components for at least some roles.
Nift Insights
What We Do
Nift helps brands thank their customers with a gift. When someone reaches a milestone, makes a purchase or completes a booking, our partners can offer them a Nift. The consumer opts in, shares what they’re into, and picks a gift they actually want from a brand they may not have met yet. Advertisers pay for the introduction to a new customer. Partners earn new revenue from moments they already have. Consumers get something good. Choice is what makes it work, and it’s why we think of Nift as an ad unit that feels like a gift. We started 11 years ago with 10 local businesses and are now on track for over $1B in gift value, with nearly 15,000 active advertisers.
Why Work With Us
Very few companies get to build something that has no direct comparison in the market. At Nift, you’ll work on a product that gives consumers a real choice, gives partners new revenue, and gives advertisers a better way to meet new customers. Nift began with 10 local businesses and a simple belief in appreciation, and 11 years later that idea stil
