PhantomBuster

• Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems. • Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features. • Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows. • Pre-process data by using techniques such as data cleaning, feature engineering, and transformation. • Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production. • Monitor, debug, and continuously improve deployed models and AI tools. • Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML). • Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy.

• Ownership of features for PhantomBuster and our new AI product, handling projects hand in hand with your Product Manager and your Squad Lead to produce solutions that work for our customers. • Maintain and develop high-quality front-end features and Design System • Improve and maintain the test suite (unit → E2E) • Participate in the support duty of the team (report and fix bugs) • Be a champion AI-assisted engineering: from code generation and testing to debugging

• Develop product increments, handling projects hand in hand with your Product Manager and your Squad Lead to produce solutions that work for our customers • Challenge product and technical features, help the team to make sure we focus on the right thing at the right time • Maintain and develop high-quality front-end features and Design System • Review the work of the team with a focus on feature quality and maintainability • Participate in the support duty of the team (report and fix bugs) • Improve the test suite from unit tests to end-to-end tests • Write Decision Records and relevant technical documentation • Keep an eye on our development cycle to raise issues if needed • Be a champion AI-assisted engineering: from code generation and testing to debugging • Establish guardrails for safe and effective use of AI (human review, bias detection, security) • Identify opportunities to leverage AI internally (developer productivity, monitoring, anomaly detection)

• Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems. • Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features. • Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows. • Pre-process data by using techniques such as data cleaning, feature engineering, and transformation. • Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production. • Monitor, debug, and continuously improve deployed models and AI tools. • Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML). • Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy.

• Organic growth strategy (end-to-end) • Scale and evolve our content production engine • Build a new engine from scratch • Win in AI search (AEO) • Grow and diversify our organic growth channels • Own the technical foundations

• Handle tickets — with care and depth • Investigate, troubleshoot, and resolve advanced tickets escalated from Level 1 or coming directly from users • Handle a variety of query types: product behaviour, integrations, Account and Billing questions, and automation edge cases • Communicate solutions clearly and patiently — no matter how technical the issue or how frustrated the user • Escalate to Level 3 when needed, with a well-structured handoff • Be a resource for the Level 1 team, supporting them with product knowledge and complex case guidance • Become the product expert you're hired to be • Develop mastery of the PhantomBuster platform — how it works, where it breaks, and why • Build a solid understanding of the lead generation workflows our users are trying to build, not just the product itself • Stay ahead of product changes and new features so users always get accurate, up-to-date answers • Feed intelligence back into the business • Label, collect, and structure user feedback and recurring issues to share with the Product team • Identify patterns in ticket volume, error types, or user confusion that signal product or documentation gaps • Contribute to Customer Care improvement initiatives • Help evaluate and improve our AI chatbot responses to ensure users get accurate, helpful answers at Level 0 • Participate in quality reviews to ensure the standards we've set are consistently met across the team • Create and update internal documentation, troubleshooting guides, and process notes for the broader Customer Enablement team

• Own and optimize paid acquisition • Manage and improve campaigns across Google Ads, Meta Ads, and LinkedIn • Run structured experiments on targeting, positioning, creative, and landing pages • Collaborate on ad creatives and landing page improvements using tools like Canva, Figma, and AI-assisted workflows • Use paid channels to test ICP hypotheses and messaging • Connect paid performance to activation and retention metrics • Drive experimentation across the full funnel • Contribute across acquisition, activation, and retention • Identify friction in signup, onboarding, and pricing flows • Run experiments related to referrals, affiliates, product-led loops, churn reduction, and reactivation • Document hypotheses clearly and scale what works • Support early-stage growth for a new product • Explore potential distribution channels before heavy execution • Test early-stage acquisition hypotheses across communities, partnerships, and product loops • Design lean experiments focused on traction and learning • Operate comfortably in ambiguity and iterate quickly • Use data to drive decisions • Analyze performance data and translate it into actionable recommendations • Build and maintain dashboards in Looker Studio • Track KPIs across the funnel • Share structured learnings that influence prioritization

• Conduct descriptive and exploratory analyses to support product, growth, and business decisions. • Build and maintain dashboards and reports to monitor KPIs, experiments, feature launches, and overall performance. • Partner with Product and Growth teams to analyze user behavior, funnels, and retention. • Design and maintain clean, well-documented data models in Snowflake and dbt. • Improve metric definitions and ensure consistency across teams and dashboards. • Contribute to the reliability and scalability of our analytics infrastructure. • Support experimentation efforts (A/B testing) by helping define metrics, analyze results, and interpret outcomes. • Help teams frame the right questions and turn data into clear recommendations. • Communicate insights clearly to non-technical stakeholders. • Contribute to a strong data-driven culture through documentation, presentations, and workshops. • Act as a thought partner for teams making strategic or operational decisions.