[All remote jobs](https://jobicy.com/jobs.md)[![Paddle logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/28ca3645-221.png)](https://jobicy.com/company/paddle.md)[Paddle](https://jobicy.com/company/paddle.md) # Head of Data Science Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/paddle.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md), [Portugal](https://jobicy.com/job-region/portugal.md), [Ireland](https://jobicy.com/job-region/ireland.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished15 Oct 2026Apply before14 Nov 2026Listing views60Application actions4Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Paddle is hiring a founding Head of Data Science to establish and lead its production data science capability for automated product decisioning. The role begins as a highly hands-on individual contributor position, delivering ML and agentic systems from opportunity sizing and feature engineering through deployment, monitoring, and rollback. The leader will build a team of data scientists and ML engineers after initial use cases demonstrate measurable commercial value. Key applications span payments performance, revenue recovery, growth, monetisation, sales operations, and regulated risk decisioning. Success depends on rigorous experimentation, production reliability, financial value measurement, and close partnership with Product, Engineering, Finance, Risk, Legal, and Compliance. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a greenfield executive-level technical role requiring the candidate to personally build and operate the first production systems while defining strategy, governance, platform standards, and a future team. It requires deep expertise across ML, agentic workflows, experimentation, financial impact measurement, and regulated payments environments. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$230,000US market range (per year)$180k–$300k0$330k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation is disclosed. The displayed figures are AI-estimated annual USD market compensation for a senior, hands-on Head of Data Science leading production ML and agentic decisioning in a fintech context; actual pay will vary by eligible hub, local currency, equity, and experience. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Agentic AI Systems](https://jobicy.com/jobs?search_keywords=Agentic%20AI%20Systems.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Model Monitoring](https://jobicy.com/jobs?search_keywords=Model%20Monitoring.md)[Drift Detection](https://jobicy.com/jobs?search_keywords=Drift%20Detection.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[Uplift Modeling](https://jobicy.com/jobs?search_keywords=Uplift%20Modeling.md)[Data Science Leadership](https://jobicy.com/jobs?search_keywords=Data%20Science%20Leadership.md)[ML Engineering](https://jobicy.com/jobs?search_keywords=ML%20Engineering.md)[Payments Analytics](https://jobicy.com/jobs?search_keywords=Payments%20Analytics.md)[Model Governance](https://jobicy.com/jobs?search_keywords=Model%20Governance.md)[Financial Impact Analysis](https://jobicy.com/jobs?search_keywords=Financial%20Impact%20Analysis.md) Sample interview questionsHow would you select the first automated decisioning use case for Paddle?I would create a value-based backlog that estimates addressable impact, confidence in the causal pathway, data readiness, implementation effort, operational risk, and time to learning. I would validate the leading options with retrospective analysis and back-testing, align the measurement methodology with Finance, and choose a use case that can be safely shadow-tested before full deployment. Describe how you would take a payment-performance model from concept to reliable production operation. I would begin with an explicit decision policy and success metric, build historically accurate training data and features, and establish offline evaluation against an incumbent strategy. After deployment through a controlled shadow or A/B test, I would implement monitoring for data quality, latency, availability, model performance, drift, and economic outcomes, with documented escalation, retraining, rollback, and incident-response procedures. When would you choose rules, a conventional ML model, or an agentic system? I would use rules when the logic is stable, understandable, and captures most of the available value. I would use conventional ML when predictive or causal signal can improve repeatable, high-volume decisions, and use an agent only where tool use, unstructured context, or multistep reasoning provides material incremental value that can be evaluated and observed safely. How would you prove that a deployed model created financial value? I would define the counterfactual and financial methodology with Finance before launch, then use randomized A/B testing where feasible or carefully designed quasi-experimental methods where it is not. I would translate metric movement into incremental revenue, recovered payments, reduced losses, or cost savings, account for implementation and operational costs, and report confidence intervals and realised value on a recurring basis. How would you build a data science team after delivering the first use cases yourself? I would hire senior data scientists into the highest-value domains while centralising ML engineering, reusable platform components, governance, and technical standards. The team would share consistent practices for experimentation, production ownership, evaluation, documentation, monitoring, and incident response, while remaining embedded with the product and operational partners accountable for each business outcome. Opportunity details ## About this role. What do we do? Paddle offers digital product companies a completely different approach to their payment infrastructure. Instead of assembling and maintaining a complex stack of payments-related apps and services, we’re a Merchant of Record for our customers. That means we take away 100% of the pain of payment fragmentation. It’s faster, safer, cheaper, and, above all, way better. We’re backed by investors including KKR, FTV Capital, Kindred, Notion, and 83North and serve over 10,000 software sellers in 245 territories globally. The role: We are looking for a Head of Data Science to build Paddle’s data science capability from the ground up. Our Merchant of Record model gives us a data position no PSP or billing provider has: subscription and pricing context alongside granular payment-outcome data, across thousands of software businesses. This role exists to turn that into economic value by putting machine learning and agentic systems into production. The mandate is deliberately narrow and deliberately ambitious: data science at Paddle owns automated decisioning inside the product — traditional machine learning and agentic systems alike — not decision-support analytics. We have a long list of candidate opportunities than we can fund, spanning payment performance and revenue recovery, sales and marketing operations, growth and monetisation intelligence, and risk, trust and compliance. We have a working hypothesis about which of these pays back first, and we’ll share it — but part of the job in your first quarter is to pressure-test it, size the alternatives yourself, and tell us where to start. This is a founding role, and for the first few quarters it is a building role more than a managing one. You’ll be the only person in the function: doing the analysis, engineering the features, training and evaluating the models or agents, taking the first system live with our engineering teams — and then operating it, answerable for its uptime, its drift and its numbers. Once the first use cases are proving out, you’ll hire and lead a hub-and-spoke team of data scientists and machine learning engineers embedded across our highest-value business areas. You’ll report into the VP of Data and work in close partnership with Product, Payments, Engineering, Risk and Finance. What you’ll do: * Prioritise which opportunities have the biggest impact.. Build a value-based use-case backlog across payment performance and revenue recovery, sales and marketing operations, growth and monetisation intelligence, and risk, trust and compliance; size the leading candidates properly; and make the call on sequencing with the relevant Product, Risk, RevOps and Finance stakeholders. This gets refreshed quarterly. * Personally deliver the first system end to end: the analysis and back-test, the features, the model, policy or agent, the deployment, and the shadow and A/B tests that prove it works. Not a spec handed to someone else to build. * Operate what you deploy. Own monitoring, retraining, drift response, incident handling and rollback for live decisioning, alongside the engineering teams whose services call it — and set the expectation that the function runs its systems rather than shipping them. * Work across both traditional ML and agentic systems, and be honest about which a problem actually needs: a propensity or uplift model, a policy of rules, or an agent with tools, evals against golden answer sets and trace-level observability. Several of our strongest candidate use cases point each way. * Build and lead the team — hire senior data scientists embedded in value areas and machine learning engineers in the hub, and set the professional standards, shared methods and reusable components the function runs on. * Establish the production stack alongside Data Platform and Engineering: reproducible training data, code and artefacts; a model registry; inference services with real latency, availability and rollback requirements; historically accurate features where decisions need backdated reconstruction; eval harnesses and trace observability for agentic workflows; and monitoring for data quality, drift, model performance and economic outcomes. Start ad hoc where that’s sufficient and platformise once the first use cases have shown what’s actually needed. * Own value capture end to end. Shadow-test and A/B test every deployment against the incumbent strategy, translate metric movement into a financial number on a methodology co-signed by Finance, and publish a quarterly report on realised value. * Set the governance model for automated decisioning — proportionate risk assessment, clear ownership, latency and availability requirements, human escalation and rollback — working with Legal, Privacy, Compliance and Risk on GDPR, EU AI Act and payments obligations, and producing the evidence early enough to shape the design. * Define the boundaries and the working relationship with Product Science, Analytics Engineering, Data Platform and AI Enablement, so accountability for decision support versus automated decisioning stays unambiguous. * Deliver cross-functionally: embed in delivery groups with product owners, domain experts and platform engineers rather than handing models over the wall. We’d love to hear from you if you: * Experienced leading data science or ML teams that own systems in production, with deployments that moved a commercial metric and kept running afterwards. Proofs of concept and dashboards are not what we’re hiring for. * Hands-on now, not formerly. Your first quarters are spent writing SQL and Python, engineering features, evaluating models and agents, and doing the work that gets a system live — not reviewing someone else’s. * Experienced across both traditional ML and agentic systems, and clear about how they differ in practice: propensity and uplift models, feature pipelines and drift on one side; tool and context design, prompt and retrieval iteration, evals against golden answer sets and trace observability on the other. * Practised at running live systems rather than just launching them — monitoring, retraining, incident response, rollback, and the on-call reality of a decision the business depends on. * Pragmatic about method: happy to argue for rules or heuristics where they capture most of the value, and to reserve models and agents for where they genuinely earn their keep. * Comfortable with an undefined starting point. There’s a long list of candidate use cases and a strong internal hypothesis, but no fixed roadmap — you’ll be expected to form your own view, back it with numbers, and defend it to an executive audience. * Strong on measurement — experimentation, uplift modelling, back-testing — and willing to hold your own work to a financial number that Finance will scrutinise. * Fluent in the engineering side of production ML and agents (training pipelines, model registries, inference services, feature stores, drift detection, trace observability) and able to hold your own with senior engineers on latency, availability, observability and rollback. * Able to hire, level and develop senior data scientists and ML engineers, and to set standards for a function that doesn’t exist yet. * Credible with executives and commercial stakeholders: you can take a use case from business problem to value estimate to prioritisation decision, and say no to low-value work. * Experienced in payments, fintech, subscriptions, high-volume commercial operations or another regulated transactional domain — enough to get to a credible value estimate in a new area quickly. * Comfortable treating regulated ML as a design constraint rather than paperwork — model risk assessment, DPIAs, auditability, traceability of model and policy versions to production decision logic. Everyone is welcome at Paddle At Paddle, we’re committed to removing invisible barriers, both for our customers and within our own teams. We recognise and celebrate that every Paddler is unique and we welcome every individual perspective. As an inclusive employer, we don’t care if, or where, you studied, what you look like or where you’re from. We’re more interested in your craft, curiosity, passion for learning and what you’ll add to our culture. We encourage you to apply even if you don’t match every part of the job ad, especially if you’re part of an underrepresented group. Please let us know if there’s anything we can do to better support you through the application process and in the workplace. We will do everything we can to support any accommodations needed. We’re committed to building a diverse team where everyone feels safe to be their authentic self. Let’s grow together. Why you’ll love working at Paddle We’re a diverse, growing group of Paddlers who pride ourselves on our transparent, collaborative and respectful culture. We live and breathe our values, which are: * Paddle Together – “None of us, is as smart as all of us” * Paddle Simply – “Simple can be harder than complex: you have to get your thinking clean to make it simple” * Paddle for others – “We can realise our wildest dreams, so long as we help enough other people to realise theirs” We offer a full suite of benefits, including attractive salaries, stock options, unlimited holidays, enhanced parental leave, retirement plans, private healthcare and well-being initiatives. We invest in learning and will help you with your personal development via constant exposure to new challenges, an annual learning fund, and regular internal and external training. Connecting at Paddle We’re a ‘digital-first’ company, which means our Paddlers always have the freedom to choose where, when and how they work within our hub locations. We recognize that where our Paddlers do their best work is unique to them and we provide trust and autonomy for everyone to work in a way which fits them best. We also value the power of connection and have invested in global hubs which help local communities flourish and bring us together for focused collaboration. Our Paddle hubs: * In Europe: UK (London headquarters), Portugal (Lisbon hub), Netherlands and Ireland (Dublin hub) * In North America: Austin, TX (USA) and Toronto (Canada) Our hub philosophy creates an engaging experience for all Paddlers, wherever, whenever and however we choose to work. To support this, we focus hiring within our hubs and will always openly share the available locations for each open position on our job description. Show more [Apply now >](https://jobicy.com/jobs/155403-head-of-data-science-2.md) > Annual salary information is not provided for this position. 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