Senior Data Scientist, Growth

DolarApp; Greater London; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Stock options

Discretionary bonus

Latest tools and technology

World-class team

LatAm fintech app opportunity

Office policy: 3-4 days in-office

Job summary

ARQ is hiring a Senior Data Scientist to lead data science initiatives for Growth and Marketing. You will own LTV prediction across markets and channels, partnering with Growth, Product, Finance, Data Engineering, and Engineering to turn business questions into modelling solutions.

You’ll influence growth investments, campaign evaluation, and long-term customer value, while maintaining model performance and communicating insights to stakeholders.

Qualifications

• 5+ years in Data Science, ML, Applied Statistics, Analytics, or related field.
• Experience building prediction models in consumer business (B2C/D2C), especially around LTV, growth, acquisition, retention, monetisation.
• Strong Python skills and experience handling large-scale datasets.
• Solid understanding of supervised learning, model evaluation, feature engineering, and trade-offs.
• Ability to translate ambiguous business questions into structured data problems.
• Strong business judgement and ability to link model outputs to decisions.
• Experience working cross-functionally.
• Clear communication skills to explain modelling assumptions to non-technical stakeholders.
• Comfortable in fast-moving environments with high ownership and evolving priorities.
• Fluent in English.

Responsibilities

• Design, build, maintain, and improve lifetime value prediction models across ARQ’s countries and channels.
• Collaborate with Growth, Marketing, Product, Finance, Data Engineering, and Engineering to translate needs into modelling solutions.
• Evaluate acquisition quality by channel, campaign, geography, customer segment, and product behaviour.
• Build models to inform growth spend, payback periods, customer quality, retention, and value.
• Analyze large datasets to identify patterns, risks, and opportunities.
• Monitor model performance and improve accuracy and business impact.
• Create frameworks and metrics to explain growth trade-offs.
• Work with Data Engineering to productionise models, pipelines, and reporting.
• Balance technical depth with commercial impact.
• Contribute to other DS challenges across Growth and the business.

Skills

Python

Data analysis

ML techniques

Stakeholder communication

Cross-functional collaboration

English fluency

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