Job description

WHAT WE’RE LOOKING FOR We're looking for a Senior Data Scientist to join ARQ's Growth team as our first data science hire – owning all data science initiatives across Growth and Marketing, starting with predicting customer lifetime value across our markets and acquisition channels. You'll report directly to the Head of Growth and our COO. You’ll be responsible for developing, maintaining, and improving LTV prediction models across our four countries, helping us make sharper decisions on growth investment, channel performance, acquisition quality, and long term customer value. This is a high ownership role for someone who has worked on consumer growth, LTV, acquisition, retention, or monetisation problems in a B2C or D2C environment. You should be comfortable taking ambiguous business questions, turning them into measurable modelling problems, and building solutions that influence real commercial decisions. WHAT YOU’LL BE DOING - Design, build, maintain, and improve lifetime value prediction models across ARQ’s countries and acquisition channels. - Work closely with Growth & Marketing, Product, Finance, Data Engineering, and Engineering teams to understand business needs and translate them into modelling solutions. - Help evaluate acquisition quality by channel, campaign, geography, customer segment, and product behaviour. - Build models that support better decisions around growth spend, payback periods, customer quality, retention, and long term value. - Analyse large scale customer, product, marketing, and transaction datasets to identify patterns, risks, and opportunities. - Continuously monitor model performance and improve accuracy, reliability, and business impact over time. - Create clear frameworks and metrics that help teams understand the trade offs behind growth decisions. - Partner with Data Engineering and Engineering teams to productionise models, pipelines, and reporting where needed. - Bring a pragmatic approach to modelling, balancing technical depth with commercial impact. - Over time, contribute to other Data Science challenges across Growth and the wider business. WHAT YOU’LL NEED - 5+ years in Data Science, Machine Learning, Applied Statistics, Analytics, or a related discipline. - Experience building prediction models in a consumer business (B2C, D2C), ideally around LTV, growth, acquisition, retention, churn, monetisation, or customer value. - Strong Python skills and experience working with large scale datasets. - Solid understanding of supervised learning techniques, model evaluation, feature engineering, and statistical trade offs. - Ability to translate ambiguous commercial questions into structured data science problems. - Strong business judgement and the ability to connect model outputs to real decisions. - Experience working cross functionally. - Clear communication skills, especially when explaining modelling assumptions, limitations, and recommendations to non technical stakeholders. - Comfortable operating in a fast moving environment with high ownership and evolving priorities. - Fluent in English, as we collaborate with teams across the globe. NICE TO HAVE - Experience in fintech, banking, payments, investing, lending, or another financial consumer product. - Experience at a high growth B2C or D2C company, such as consumer fintech, health tech, subscription, retail, wellness, or marketplace businesses. - Experience with LTV, CAC, payback period, acquisition efficiency, cohort modelling, retention modelling, churn prediction, or marketing mix related problems. - Experience working across multiple countries, currencies, channels, or customer segments. - Experience productionising models or working closely with Engineering and Data Engineering teams to deploy data science solutions. - Familiarity with MLOps, model monitoring, experiment design, causal inference, or incrementality measurement. BENEFITS - Competitive salary and benefits - Stock options, so you own part of what you build - Discretionary performance bonus - The latest tools and technology - A world-class team that will challenge and grow your skills - The opportunity to help build the best fintech app in Latin America - Office Policy: 3-4 days a week in-office

Requirements

Must-haves

Design, build, maintain, and improve lifetime value prediction models across ARQ’s countries and acquisition channels.
Work closely with Growth & Marketing, Product, Finance, Data Engineering, and Engineering teams to understand business needs and translate them into modelling solutions.
Help evaluate acquisition quality by channel, campaign, geography, customer segment, and product behaviour.
Build models that support better decisions around growth spend, payback periods, customer quality, retention, and long term value.
Analyse large scale customer, product, marketing, and transaction datasets to identify patterns, risks, and opportunities.
Continuously monitor model performance and improve accuracy, reliability, and business impact over time.
Create clear frameworks and metrics that help teams understand the trade offs behind growth decisions.
Partner with Data Engineering and Engineering teams to productionise models, pipelines, and reporting where needed.
Bring a pragmatic approach to modelling, balancing technical depth with commercial impact.
Over time, contribute to other Data Science challenges across Growth and the wider business.
5+ years in Data Science, Machine Learning, Applied Statistics, Analytics, or a related discipline.
Experience building prediction models in a consumer business (B2C, D2C), ideally around LTV, growth, acquisition, retention, churn, monetisation, or customer value.
Strong Python skills and experience working with large scale datasets.
Solid understanding of supervised learning techniques, model evaluation, feature engineering, and statistical trade offs.
Ability to translate ambiguous commercial questions into structured data science problems.
Strong business judgement and the ability to connect model outputs to real decisions.
Experience working cross functionally.
Clear communication skills, especially when explaining modelling assumptions, limitations, and recommendations to non technical stakeholders.

Nice-to-haves

Experience in fintech, banking, payments, investing, lending, or another financial consumer product.
Experience at a high growth B2C or D2C company, such as consumer fintech, health tech, subscription, retail, wellness, or marketplace businesses.
Experience with LTV, CAC, payback period, acquisition efficiency, cohort modelling, retention modelling, churn prediction, or marketing mix related problems.
Experience working across multiple countries, currencies, channels, or customer segments.
Experience productionising models or working closely with Engineering and Data Engineering teams to deploy data science solutions.
Familiarity with MLOps, model monitoring, experiment design, causal inference, or incrementality measurement.

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Keywords to mirror

machine learningpython

Platform: ashby
Effort: quick

Benefits

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