Role Description Join our Data Science team and become a key part of our product squads, delivering features that enrich our product and uphold the first-class experience we're known for. As a Head, you will balance hands-on technical work with managerial responsibilities, having the opportunity to make a significant impact through both technical excellence and strategic leadership. You will drive high-impact projects, mentor team members, and play a crucial role in shaping the technical direction of our data science and machine learning solutions. - Manage a team of Data Scientists and Machine Learning Engineers, fostering a high-performance culture and continuously raising the bar for technical excellence. - Act as a technical reference for the team, architecting, developing, and deploying machine learning systems to production when needed. - Discuss business requirements with Product Managers and other senior leaders to align data strategy with business goals. - Guide the team in designing and executing experiments, ensuring robust analysis of structured and unstructured data to improve current models. - Focus on recruiting great talent and helping them grow through regular performance feedback and development opportunities. - Set clear goals and objectives for the team and ensure they are aligned with the overall business objectives. - Advocate for the value of data and AI within the organization and foster a data-driven culture. Qualifications - You have a proven track record of leading teams and managing careers within the data field for at least 4 to 5 years. - Deep understanding of machine learning concepts, including regression, classification, clustering, neural networks, cross-validation, and model explainability. - Strong knowledge of probability and statistics, including experimental design, optimization, and causal inference for A/B testing. - Solid understanding of the engineering challenges required to deploy and maintain machine learning systems in production. - Proficiency in Python and experience with software engineering best practices. Requirements - Have experience building solutions with Large Language Models (RAG, fine-tuning) and developing AI agents or agentic frameworks (LangGraph, LangChain). - Hold an MSc. or PhD in Computer Science, Statistics, or a relevant technical field. - Have solid experience with Deep Learning and NLP techniques. - Have experience working in fast-growing startups or high-tech companies. - Excellent written and verbal communication skills in English are mandatory. Benefits - Competitive salary - Profit sharing - Meal allowance - Health insurance - Dental plan - Life insurance - Childcare subsidy and Atypical Parenthood subsidy - Wellhub - Home office allowance - Employee assistance program (mental health, social, legal, and financial support) - Extended parental leave - Day off on birthday, Mother’s Day, and Father’s Day - Benefits Club (discounts on everyday services) - Discounts at educational institutions - Reading kit for children – PlayKids

• Build responsive web applications, internal tools, and landing pages primarily through AI coding tools such as Cursor, Lovable, Bolt, or v0. • Translate written briefs, feature requests, and client roadmaps into working, deployable software. • Prompt iteratively and refine AI-generated output to meet functional and design requirements. • Review, test, and debug AI-generated code to ensure it is correct, secure, and maintainable — not just “it ran once.” • Integrate front-end builds with the back-ends, agents, and retrieval services built by the Applied AI and Integration engineers so data and AI features flow end to end. • Recognize where vibecoding is the right tool and where a problem needs hand-written code or a teammate’s help, and escalate accordingly. • Deploy applications, gather feedback, and iterate quickly. • Document your work in Notion so others can pick it up.

• Architect, manage, and scale AI workflows that supercharge content creation, campaign execution, and sales enablement, such as drafting assistants, content repurposing pipelines, and lead-scoring agents. • Hunt down repetitive, high-volume tasks ripe for automation, then design the solution, deploy it, and prove the impact. • Evaluate and weave cutting-edge AI tools and agents into our marketing stack. • Craft and fine-tune prompts, set smart guardrails, and document workflows so the whole team can run with them confidently and consistently. • Safeguard output quality, accuracy, and brand consistency, continuously tuning workflows as the technology and our needs evolve. • Take full ownership of HubSpot day to day, using AI to streamline and automate contact properties, lifecycle stages, lead statuses, workflows, email campaigns, and reporting. • Partner with the Operations team and external partners to build AI SDR agents that scale our qualification process and outreach. • Harness AI to build and maintain dashboards and reports that deliver crystal-clear visibility into pipeline, campaign performance, and the marketing KPIs that matter. • Connect HubSpot seamlessly with our marketing and AI tools, keeping data clean, structured, and well governed. • Document CRM processes so teams across every region run from one consistent playbook. • Partner closely with marketing, operations, sales, and product teams to turn ideas into results. • Drive projects to the finish line on time, with clear, consistent communication across every stakeholder.

• Build and scale a predictable pipeline generation engine through affiliates, influencers, creators, podcasters, referral partners, strategic alliances, events, and industry relationships. • Generate qualified opportunities for the sales team and own accountability for marketing-sourced pipeline and revenue. • Own marketing budget allocation and channel strategy with a focus on ROI and revenue outcomes. • Build repeatable acquisition systems that reduce dependence on founder-led growth. • Develop website messaging, positioning, and content that improve conversion rates and increase qualified inbound demand. • Build SEO, AEO, and LLM visibility strategies that drive measurable pipeline growth. • Use AI and automation to increase output, improve efficiency, and create scalable growth systems. • Partner with leadership to establish revenue targets, pipeline goals, channel KPIs, and forecasting models.