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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** SPEAK TO THE KING LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $94,589.0 - $111,502.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Fraud Prevention and Detection, Python (Programming Language), Recommender Systems, Standard Sql, Transaction Data, Unstructured Data, Feature Engineering, Model Validation, Performance Monitor - **Published:** October 11, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pqfmiy4cgc ## About the Role A fast-growing fintech is hiring a Senior Data Scientist on a permanent, full-time basis in London, with at least three days a week in the office (Mondays are a fixed in-office day for the whole team). The salary is £94,589 to £111,502 depending on experience, with stock options of £80,479 to £105,003 on top. Benefits include 35 days holiday, private healthcare with Bupa, up to 12 weeks enhanced parental leave, a £750 annual learning allowance, and a cycle-to-work scheme., Must-haves * Predictive models shipped to production and driving real decisions * End-to-end ownership: feature engineering through post-deployment monitoring * Python fluency and strong SQL * Deep understanding of model evaluation beyond headline accuracy: calibration, stability, drift * Comfortable with ambiguous problem statements and messy, unstructured data * Able to communicate model outputs and methodology clearly to non-technical stakeholders * Commercial mindset: optimises for decision quality, not modelling sophistication Nice-to-haves * Experience in consumer lending, credit cards or fintech * Hands-on work with bureau data and behavioural credit models * Background in fraud detection or collections modelling * Experience with transaction data or customer recommendation systems ## Description This role exists because the business is still building its predictive modelling capability, not inheriting a finished one. You will own the full modelling lifecycle across credit acquisition, portfolio management, collections, fraud and rewards: from initial hypothesis and feature engineering through to production deployment, monitoring and iteration. Your outputs feed directly into a credit strategist's decision-making, so clarity and usability matter as much as model performance. You will report to the CRO and work shoulder-to-shoulder with the teams making live commercial decisions. If you want to build something that genuinely shapes how a consumer credit business runs, rather than polish models in a central team, this is the role., * Build and continuously improve acquisition models using bureau, application and alternative data to assess applicant risk, affordability and expected customer value * Develop behavioural models for portfolio management, helping the credit strategy team improve credit profitability * Build collections segmentation models that predict member responses to different interventions and focus effort where it has the greatest impact * Develop and improve fraud detection models, identifying suspicious behaviour earlier and reducing friction for genuine members * Work with AI engineers to use member data to surface the right reward at the right time * Own the full modelling lifecycle: data exploration, feature engineering, training, validation, implementation, and ongoing performance monitoring * Get models into production yourself, not hand them off to engineers * Identify additional internal signals, bureau attributes and external data sources that could materially improve prediction * Build robust model monitoring and validation so the team understands calibration, discrimination and drift after deployment * Explain model outputs and methodology clearly to the CRO and committee members without relying on technical language * Help establish the modelling standards, tooling and experimentation practices that will define how data science works at the business, * Acquisition, collections and fraud models are live in production within the first six to twelve months, with measurable impact on decisions * The credit strategist has clear, usable model outputs they can act on with confidence * Models are monitored consistently and iterated on when performance drifts * The CRO and committee members can follow and interrogate model logic without needing a technical interpreter * Modelling standards, tooling and development practices are meaningfully more mature than when you joined Team and Culture * Small, senior team where you work directly with the people making commercial decisions rather than through layers of stakeholders * Office-first culture, with genuine emphasis on proximity between data science and the teams using its outputs * The CRO owns both risk and analytics, so data science sits at the centre of the business rather than at the edge of it * Builder's culture: speed and real-world impact are valued over theoretical perfection Challenges * The modelling stack is still being built, so you will need to create structure and standards as well as deliver models * The remit is broad: acquisition, portfolio management, collections, fraud and rewards all sit within scope for one person * Working with ambiguous problem statements and incomplete data is the norm, not the exception * Stakeholder communication is a genuine part of the job: explaining complex modelling decisions to a CRO and committee requires consistent clarity and confidence