Sift is the leading fraud prevention platform delivering digital trust to 700+ global brands, allowing them to grow confidently by stopping fraud while enabling excellence in customer experience. Backed by a global data network of over one trillion annual events, Sift helps companies convert risk into revenue and scale without compromise. Brands including Hertz, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences.

Sift

Equity / Stock Options

 At Sift, the Product Management team owns the vision and oversees the execution of all of Sift’s products, from ideation to product launch.

We are the center of the product life cycle, working with engineering, sales, services and marketing along continually engaging with our customers. We are dedicated to safeguarding our customers’ digital platforms by preventing unauthorized account access and detecting malicious, fraudulent activities. Our PMs are product specialists, initiative owners, strategic thinkers, and customer advocates who shape not just what we build, but how it changes the industry.

 We’re looking for a seasoned Product Manager to work across Sift’s product portfolio and key strategic initiatives. This is a broad, high-impact role for a PM who thrives in both the details of product development and the dynamics of go-to-market strategy. You will partner with engineering teams to ship great product, while also working directly with customers, sales, and marketing to ensure what we build lands with maximum impact.

You won’t just own a single product — you’ll help shape the direction of the business, drive cross-functional alignment, and represent the voice of the customer across everything Sift ships. You’re someone who can move fluidly between a technical architecture discussion in the morning and a customer discovery call in the afternoon.

Own the Product Vision Across Initiatives: 

Partner with engineering and leadership to define and articulate the “why” and “what” across multiple product areas and strategic initiatives. Use data, market research, and customer insight to set direction and prioritize ruthlessly.

Drive the Roadmap with Business and Technical Clarity: 

Own product roadmaps that reflect both customer needs and business objectives. Communicate the rationale behind prioritization decisions clearly to engineering, executives, and go-to-market partners alike.

Engage regularly and deeply with customers — from discovery conversations to beta programs. Validate hypotheses, surface unmet needs, and ensure the customer perspective is embedded in every product decision.

Write clear, detailed specifications. Create tickets, draft copy, define acceptance criteria, and partner closely with engineering to ship high-quality product on time.

Work hand-in-hand with Sales, Marketing, and Customer Success to ensure new features and products are positioned and launched effectively. Support deal cycles, create GTM materials, and drive adoption of what you ship.

Rally engineering, design, data science, and business stakeholders around a shared vision. Be a clear communicator and a collaborative leader who helps teams do their best work.

Help identify new market opportunities, inform product strategy at the portfolio level, and contribute to Sift’s long-term product direction.

5+ years of Product Management experience in B2B SaaS, ideally in fraud prevention, trust & safety, fintech, or adjacent domains

Proven ability to own multiple products or workstreams simultaneously, balancing short-term delivery with long-term vision

Strong track record of converting customer insights and business requirements into successful product features

Experience working closely with engineers, data scientists, and designers — you are comfortable in technical conversations without needing to be an engineer

Demonstrated ability to partner with Sales, Marketing, and Customer Success to drive GTM execution, launch, and adoption

Excellent communication skills — you can present strategy to the C-suite, write a tight product spec, and run a customer interview all in the same week

Data-driven mindset with experience using analytics and customer feedback to inform decisions

Experience in fraud prevention, identity, or payments

Familiarity with machine learning concepts and working with data science teams

Experience with enterprise customer-facing work: QBRs, Design Partner and Early Access projects, advisory boards, etc.

At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.

This document provides transparency around how Sift handles the personal data of job applicants: 

Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at 

Senior Level

Experience

Sift is seeking a seasoned Product Manager to oversee strategic initiatives across its AI-powered fraud platform. The role involves defining product vision, managing roadmaps, and partnering with engineering and GTM teams to ship high-impact features. Key requirements include 5+ years of B2B SaaS experience and a data-driven approach to product development.

Estimated On Target Earnings $160K – $220K • Offers Equity

Product Manager

Full Time

At Sift, our mission is to help businesses thrive and trust their customers through our innovative fraud detection and prevention solutions. We are a committed team of professionals driving the fight against online fraud and abuse, fostering a safer digital world. Our collaborative and inclusive workplace is a hub for creativity and innovation.

We are seeking a dynamic and outcome-driven Professional Services Consultant specializing in fraud detection and prevention to join our team at Sift. The ideal candidate will possess a blend of technical acumen, strategic thinking, and exceptional communication skills to deliver comprehensive, client-focused fraud solutions. As a Professional Services Consultant, you will play a pivotal role in ensuring the successful implementation and optimization of our fraud solutions, driving client satisfaction and business success.

Monitor project progress, mitigate risks, and ensure on-time delivery within scope and budget.

Collaborate with internal teams and stakeholders to ensure alignment and support throughout the project lifecycle.

Lead the planning, coordination, and execution of fraud solution implementation projects.

Customize Sift's fraud prevention solutions to meet specific customer requirements and ensure seamless integration with their existing systems to drive desired business outcomes.

Develop and deliver comprehensive training programs and documentation on fraud detection and prevention for end-users.

Present tailored fraud prevention strategies and solutions to key stakeholders.

Stay current with industry trends, technology advancements, and best practices in fraud detection and prevention to provide thought leadership and strategic guidance.

Work with product development teams to relay customer feedback and contribute to product innovation in fraud prevention.

Work closely with across the business (Customer Success, Sales, Support, Engineering, etc.) to ensure alignment on being up to date with latest best practices to deliver the best solution to customers.

Prepare and present detailed reports on project outcomes, performance metrics.

Bachelor's degree in Business, Information Technology, Computer Science, or a related field.

Minimum of 5 years of experience in professional services, consulting, or a similar customer-facing role in SaaS.

Experience in fraud detection and prevention.

Strong project management skills with a proven track record of delivering successful fraud prevention projects.

Excellent communication, presentation, and relationship-building skills.

Technical proficiency in fraud detection tools and technologies, including but not limited to machine learning, data analytics, REST APIs and/or customer API libraries, JSON, and CRM systems.

Ability to work independently and as part of a collaborative team.

Strong problem-solving skills and a proactive approach to identifying and addressing customer needs.

Ability to work flexible hours to provide coverage to APAC customers.

PMP or other relevant project management certification.

Experience in specific industry sectors such as e-commerce, finance, fintech or cybersecurity.

Advanced degrees or continuous education certifications in relevant fields.

Travel Required

Education

Shift Time

Sift is seeking a Professional Services Consultant to drive the implementation and optimization of fraud detection and prevention solutions. This remote role involves managing complex projects, customizing SaaS solutions, and collaborating with cross-functional teams to ensure client success. Candidates should possess strong technical acumen in REST APIs, data analytics, and machine learning within a cybersecurity context.

Estimated On Target Earnings $130K – $185K • Offers Equity

Professional Services Consultant 

Our Data Science team owns the machine learning backbone of Sift's fraud platform—a system that learns from 1T+ events annually across our network of 700+ global customers. You'll work alongside ML engineers, platform teams, and customer success leads who obsess over reducing false positives while catching sophisticated fraud patterns at scale.

We're looking for a specialist who combines exceptional statistical rigor with deep fraud and information security domain expertise. You understand account takeover tactics, payment fraud vectors, identity manipulation, and network abuse patterns—not from reading threat reports, but from having modeled them in production. You'll be the go-to expert for diagnosing why models fail, architecting solutions across multiple modeling paradigms, and building processes that prevent data science from becoming a bottleneck. Your domain knowledge becomes a force multiplier: you'll spot feature opportunities others miss, anticipate how adversaries will probe your models, and translate customer fraud signals into modeling advantage.

 Models that outperform baseline by measurable margins because you engineered features informed by years of fraud pattern understanding. Production systems that don't degrade 

 don't leak money to evolving fraud schemes. Teams that trust your framework recommendations because you've debugged production failures 

. A research program that uncovers untapped signal in our customer data while staying ahead of attacker sophistication.

Architect and own advanced modeling strategies

 across fraud and abuse problem domains (payment fraud, account takeover, identity spoofing, account abuse, content manipulation, credential stuffing). Your deep understanding of attacker tactics, exploit chains, and evasion strategies informs which signals matter and which are noise. You'll drive framework selection—deciding when gradient boosting on velocity features suffices, when graph neural networks unlock network effects competitors miss, when deep learning on sequence data catches adaptive fraud patterns—and hold yourself accountable for production outcomes. You'll work backward from business metrics (customer adoption, chargeback reduction, operational lift) to model objectives 

Establish and defend model quality standards that account for adversarial dynamics.

 You'll develop diagnostic frameworks to decompose model performance by fraud type, attacker sophistication level, geography, and temporal patterns. You'll own the post-launch monitoring process, identify when degradation signals retrain vs. architecture change vs. active evasion by fraud rings. You'll design sampling strategies that catch emerging fraud patterns before they scale. Your infosec intuition becomes your quality moat: you'll spot when performance drops aren't random—they're a signal that attackers have found a new exploit path.

Lead statistical innovation on our highest-leverage fraud problems.

 You'll explore novel feature representations drawn from your understanding of fraud mechanics (network propagation of compromised accounts, timing signatures of automated attacks, behavioral deviation from account history). You'll run rigorous experiments to validate whether a suspected fraud pattern is exploitable or a false lead. You'll publish findings internally (and externally where disclosable), and mentor junior data scientists on the difference between statistical significance and security-relevant signal magnitude.

Partner with ML engineering and information security on adversarial robustness.

 You'll co-design models that don't just maximize accuracy—they resist manipulation. You'll pressure-test feature importance against known evasion tactics. You'll own the handoff from research to serving, ensuring what ships hasn't leaked assumptions about attacker behavior. Your infosec depth means you're fluent in threat modeling conversations with security teams, not learning it on the job.

Build automated workflows that scale human expertise while respecting fraud complexity.

 You'll leverage AI-assisted tools (LLMs, AutoML frameworks) to accelerate experimentation while maintaining verification checkpoints informed by your domain knowledge. You'll document which automation patterns you trust (feature engineering exploration) and which require human oversight (fraud strategy pivots that might break assumptions in your features). You'll become the SME on where humans and AI each belong in fraud modeling pipelines.

Deep, hands-on knowledge of fraud and information security patterns.

 You've modeled payment fraud, account takeover, identity abuse, or network attacks in production. You understand attacker incentives, exploit chains, evasion tactics, and how fraud patterns evolve in response to defenses. You can explain the difference between a velocity signal that's correlated with fraud and one that's causal—and why attackers can't simply game it. You're not learning fraud from blog posts; you're bringing operational context from having debugged production systems under attack.

5+ years of hands-on modeling experience with production accountability.

 You've shipped models to millions of users, owned their performance in production, and made decisions based on what's broken and why—not just benchmark scores. Bonus: some of that experience comes from adversarial or security-adjacent domains.

Deep expertise in multiple modeling paradigms:

 Tree-based methods (XGBoost, LightGBM with parameter mastery), deep learning architectures (CNNs, RNNs, transformers for sequential/graph data), and graph-based methods (GNNs, message passing, network propagation). You know when each is overfit versus underspecified. You've chosen frameworks based on problem structure, not trend.

Advanced degree in Statistics, Data Science, Machine Learning, or equivalent

 (MS or PhD in quantitative field, or 8+ years of demonstrable statistical modeling depth in production fraud/security contexts). You should reason naturally about confidence intervals, bias-variance tradeoffs, and statistical significance—not just memorize formulas. We care more about statistical intuition + proven execution than pedigree.

Lean, deep statistical intuition informed by domain reality.

 You can explain why a fraud model is failing through first principles (feature leakage from attacker behavior that changed, distribution shift from geography expansion, optimization pathology from class imbalance). You spot when a metric is gaming the objective. You know the difference between a model that's broken and one that's working correctly but facing a new fraud strategy.

Proven ability to partner with AI-assisted automation tools.

 You use LLMs, AutoML, and other AI systems to move faster—especially for feature engineering exploration and pattern discovery—but you verify their outputs and know where they hallucinate or oversimplify. You're building intellectual scaffolding, not outsourcing judgment. Fraud modeling can't be delegated to automation; you're the gate.

Comfort working in ambiguity and adversarial contexts.

 You don't wait for perfect specs—you clarify what "reducing fraud leakage" means for a specific customer, run a small experiment, present findings with uncertainty bands, and iterate. You're comfortable saying "attackers might exploit this assumption" or "we need more data on this vector." You're comfortable saying "this is a business decision about fraud tolerance, not a modeling decision."

Introduction interview with the recruiter

 (45 minutes) – Learn about your background, motivation, and domain journey. Where did fraud/infosec experience come from?

 (45 minutes) – Deep dive into your approach to model design, production failure diagnosis, and threat model thinking. Discuss a time a production model failed and how you debugged it 

in the context of evolving attacker tactics

Technical problem-solving session with data scientists

 – You'll diagnose a model failure scenario 

. Why did precision drop after three months? Is it data drift, attacker adaptation, or model assumption leakage?

Fraud domain + architecture conversation with ML engineers and fraud specialists

 – Design a modeling strategy for a new fraud vector. How do you choose between tree-based, deep learning, and graph approaches? What's your threat model?

Customer impact + threat modeling conversation with product/success leads

 – How do you translate customer fraud signals into features? How do you stay ahead of attacker sophistication?

Leadership discussion with hiring manager

 – Mentorship philosophy, standards-setting, and how you'd elevate the team's threat modeling rigor.

Staff / Principal Level

Domain Expertise

Sift is hiring a Staff Data Scientist to lead advanced modeling strategies for its AI-powered fraud platform. You will architect sophisticated solutions using deep learning, graph-based methods, and tree-based models to combat evolving fraud patterns. This role requires deep statistical rigor and expertise in information security to ensure production models remain robust against adversarial attacks.

Estimated On Target Earnings $195K – $265K • Offers Equity

Staff Data Scientist

We’re hiring a Product and Customer Enablement Manager to own the strategy and delivery of learning experiences for two critical audiences: the internal teams who sell, implement, and support Sift’s products (Sales, Customer Success, Solutions, Support), and the customers who use them every day. This is a senior individual contributor role for someone who has built community and enablement programs from the ground up and is excited to do it again at a company where fraud, AI, and trust collide.

You’ll partner closely with Product, Product Marketing, Customer Success, and Sales leadership to identify capability gaps across personas, then design, build, and measure the programs that close them. You’re equally comfortable interviewing the product team about a new detection model, creating and facilitating a workshop for customer facing teams, managing an online customer community, and presenting adoption metrics to executives. You think in learning journeys, not one-off trainings — and you obsess over whether learners actually changed their behavior afterward.

If you’ve built enablement from scratch at a SaaS company, lived in an LMS, have experience in community management, and have strong opinions about what makes a great certification path, we want to talk to you.

Lead Sift’s current internal and external enablement initiative: 

Own and operationalize a high-priority enablement program spanning both internal GTM teams and customers. Partner with CX, Product, Product Marketing, Sales, Support, and Solutions to turn fragmented product knowledge into structured learning paths, launch readiness motions, customer-facing education, and measurable adoption outcomes.

Build learning journeys for multiple personas

: Define and curate role-based learning paths for internal personas (AEs, SEs, CSMs, Solutions Consultants, Support) and external personas (customer admins, fraud analysts, integration engineers, executive sponsors), tailoring content depth, format, and cadence to each audience.

Build and scale Sift’s customer education & community program

: Develop the curriculum, certifications, and self-service learning library that helps customers ramp faster, expand their use of the platform, and achieve measurable outcomes — directly supporting retention and expansion goals. Manage the Sift customer community platform and content delivery.

: Apply instructional design best practices to produce engaging, outcomes-focused content and publish them to Sift’s LMS with reliable progress tracking, scoring, and reporting, and deliver content to customer community forums, and partner enablement platforms.

: Facilitate live and recorded training sessions for new hires, product launches, customer workshops, train-the-trainer sessions, and executive briefings — adapting style and depth to the audience.

Support product enablement around launches

: Partner with Product and Product Marketing to operationalize a repeatable launch enablement motion: readiness criteria, certification gates, sales plays, and customer facing assets shipped in lockstep with every major release.

Define and track engagement and adoption metrics

: Instrument programs with clear KPIs — completion rates, certification pass rates, time-to-productivity, content engagement, NPS/CSAT on training, and downstream impact on ramp, adoption, and revenue — and report on them in regular business reviews.

: Build durable relationships with internal SMEs (Product, Engineering, CS, Sales, Support, Trust & Safety) and external stakeholders (customers, partners, industry communities) to align enablement strategy with go-to-market priorities and customer outcomes.

Champion a culture of continuous learning

: Advocate for learning as a strategic lever — running needs assessments, capability mapping, and feedback loops that keep Sift’s enablement portfolio current as the product, market, and threat landscape evolve.

•       7+ years of experience in Learning & Development, Enablement, or Instructional Design, ideally including both internal (Sales/CS) enablement and external customer education at a B2B SaaS company.

•       Proven track record of designing and launching multi-persona learning programs from scratch — not just maintaining existing curricula — and scaling them as the audience grows.

•       Experience leading large-scale enablement initiatives across both employee and customer audiences, especially during periods of product change, GTM evolution, or customer experience transformation.

•       Strong instructional design foundation with a portfolio of measurable program outcomes.

•       Experience administering and optimizing LMS systems (e.g., Absorb, Lessonly, or similar), including reporting, integrations, and content governance.

•       Demonstrated ability to define and track engagement and adoption metrics, and to tie learning outcomes to business results (ramp time, certification rates, product adoption, NPS, retention).

•       Excellent facilitation and communication skills, comfortable leading live workshops, and producing polished written content.

•       A self-starter who thrives in ambiguity, partners well cross-functionally, and can move fluidly between strategy and hands-on execution.

•       Ability to translate strategy to operational execution, who is able to demonstrate initiative, perseverance, and grit in getting things done.

•       Bachelor’s degree or equivalent practical experience; certifications such as ATD CPTD, CPLP, or instructional design credentials are a plus.

•       Bonus: Experience in fraud prevention, risk, payments, identity, trust & safety, or another technical SaaS domain; experience building certification or partner enablement programs; multilingual content delivery.

: 30-45 minutes with a recruiter to discuss your background, motivations, and the role.

: 60 minutes with the People leader to explore your enablement philosophy, experience, and fit for the role.

: 60 minutes walking through samples of your work — a certification path, or a program you built end-to-end — including the design choices and measured outcomes.

: approximately 3–4 hours covering: core role competencies & skills, cross-functional stakeholder partnership, metrics & program measurement, and values & behavior.

Sift is hiring a Product & Customer Learning Manager to lead the strategy and delivery of learning experiences for internal GTM teams and customers. The role focuses on designing instructional content, managing an LMS and customer community, and tracking adoption metrics to drive product growth. This senior individual contributor role requires extensive experience in B2B SaaS enablement.

Estimate On Target Earnings $115K – $180K

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