Staff Data Scientist

XYZ Reality

Job title: Data Scientist – Construction Tech Location: London, UK (Hybrid – in-office 3 days per week) ABOUT XYZ REALITY ────────────────────────────── XYZ Reality are the creators of the world's first and only engineering-grade Augmented Reality solution, purpose built for the construction industry. Not only have we created this holographic technology, that sits within The Atom — a smart, site-safe headset/hardhat — but we implement it on projects, utilising the power of AR to ensure that all schemes are completed in line with delivery timescales and budgets. XYZ has grown to over 100 staff across the UK, US & Europe and is working with Mission Critical organisations & construction companies to successfully deliver major projects. THE ROLE ────────────────────────────── If you're buried in construction data or project controls, producing reports that rarely leave the business — this is your chance to do something bigger. At XYZ Reality, we're looking for a Construction Data Scientist who can turn project delivery data into commercial power. You'll work with data from some of the most complex hyperscale and mission-critical construction projects in the world — uncovering trends and risk signals, and packaging them into insights that drive ABM campaigns, sales conversations, and market positioning. This is a pivotal role sitting at the intersection of delivery, revenue, and go-to-market. Your analysis won't sit in dashboards — it will shape how XYZ positions itself as the trusted intelligence layer for global construction. You'll need real construction domain knowledge, strong data skills, and the storytelling ability to make numbers move people. KEY RESPONSIBILITIES ────────────────────────────── • Query, extract, clean, and structure data from internal delivery systems using SQL and Python — running trend, variance, and comparative analysis across projects, packages, regions, and contractor delivery models to surface meaningful performance signals. • Analyse delivery metrics at individual, portfolio, and global scale — including schedule performance, productivity trends, cost and risk signals, and change or rework patterns — going beyond what happened to explain why projects perform differently. • Work directly with project managers, engineers, and delivery leads to translate on-the-ground activity into analysable data — asking the right questions, digging into anomalies, and building trust with operational teams by respecting site reality. • Package insights into clear, compelling narratives for ABM campaigns, sales enablement materials, case studies, and executive and client-facing content — positioning XYZ as the trusted insights partner for hyperscale and mission-critical construction. • Build repeatable analysis frameworks and scripts that enable continuous insight generation — moving from one-off reports to a scalable insight engine that keeps pace with a rapidly growing global project portfolio. • Collaborate with Marketing, Sales, and GTM teams to refine insight outputs based on campaign performance and commercial feedback — owning the insight narrative for the business and helping shape market positioning. REQUIRED QUALIFICATIONS ────────────────────────────── • Professional background in construction, engineering, infrastructure, or project delivery — with hands-on understanding of how projects actually run on site. Construction domain knowledge is non-negotiable; you need to speak credibly with delivery teams and understand their data. • Strong SQL skills (required) and ability to write scripts in Python, R, or similar — plus advanced spreadsheet skills (Excel / Google Sheets) and comfort working with messy, real-world datasets that lack perfect schemas or documentation. • Statistical and analytical thinking — able to identify trends, correlations, and outliers across time, geography, and project types, with a focus on interpretation and commercial implications, not just outputs. • Proven ability to translate complex findings into simple, business-relevant language and turn analysis into stories that support decision-making and revenue. Experience supporting commercial, marketing, or GTM functions is a strong advantage. KEY EXPERIENCE & SKILLS ────────────────────────────── • Construction domain knowledge — project delivery, packages & site reality • SQL (required) & Python / R scripting for data extraction & analysis • Delivery performance analytics — schedule, productivity, cost & risk • Data storytelling — turning insight into ABM, sales & exec narratives • Cross-functional collaboration — delivery, revenue & GTM teams • Repeatable frameworks — scalable insight engines, not one-off reports BENEFITS ────────────────────────────── 🏝️ 25 days annual leave + public holidays 🩺 Private healthcare with Vitality 🎄 Christmas shutdown days on top of leave allowance (2–4 per year usually) 🚇 Office located within a 5-minute walk from Angel station 🏠 Hybrid working 🪙 Biannual salary reviews 🥳 Summer & Christmas staff parties 🍣 Free lunch bought in and after-work gathering/drinks every Thursday in the office 💰 Employee referral scheme 🚲 Cycle to Work scheme 🚀 Make a real-world impact of revolutionising the construction industry

Wayflyer

About Wayflyer Today's small businesses need a capital provider that keeps pace with their growth ambitions. Traditional financing options are slow, cumbersome and often out of reach. That's why we built Wayflyer. Our technology allows us to assess businesses in minutes, generate financing offers that reflect their growth potential and send funds in as little as 24 hours. To date, we've deployed over $6bn to thousands of businesses worldwide, backed by Tier 1 banks like J.P. Morgan. You'll be collaborating with ambitious colleagues from around the world. We have offices in Dublin, London, New York, Charlotte, Berlin and Sydney. The challenge Wayflyer has deployed over $6bn in funding and we're not slowing down. Every credit decision we make is a bet on a business's real trajectory, and the models behind those bets need to get sharper and faster than they were yesterday. We're building the next generation of commercial credit, with AI running through every layer of the stack. What you'll actually do You'll own the full lifecycle of our credit risk models. We treat Machine Learning as a branch of software engineering here, so you'll write production-grade code, not just notebooks. * Lead the end-to-end build of credit risk models, treating ML as software engineering so everything you ship is fast, reproducible and robust. * Design modelling frameworks for decisioning, pricing and fraud that move company-wide P&L, not just a dashboard metric. * Build credit policies and lending strategies that expand our addressable market safely, pushing conversion and profitability up together. Scientific integrity is non-negotiable. Model improvements need to be statistically significant, with no data leakage and no bias hiding in the numbers. You'll turn technical findings into business trade-offs leadership can act on, mapping ROC AUC straight to revenue and loss, and you'll set the bar for code quality: reviewing, mentoring, and contributing to the libraries the whole team relies on. You'll stand out if you've worked on highly automated lending, price sensitivity modelling, or Customer Lifetime Value. What this role could turn into Senior Data Scientists here tend to grow into Staff or leadership roles with broader influence over risk strategy and team direction. We're building a quantitative risk function that genuinely drives P&L. The people who shape it early will have significant influence over where it goes. Who thrives here * Gets restless when a model is "good enough" but not great * 4+ years building and maintaining production-grade ML systems * Cares as much about code quality as the statistics behind it * Strong grip on predictive modeling and credit risk concepts (IV, ROC AUC, SHAP, PD, EAD, LGD, EL) * As comfortable in a modern monorepo (Python, SQL, Snowflake, dbt, and the rest of the stack) as a software engineer would be * Would rather walk a commercial stakeholder through a ROC AUC curve and make them care about the revenue impact than hide behind jargon * Uses AI as an accelerant, not a crutch * Comfortable owning outcomes, not just tickets We hire for range You'll need real depth on credit risk concepts (PD, EAD, LGD), a solid grasp of modelling techniques like IV and SHAP, and comfort working across Snowflake, dbt, ZenML, Dagster and Weights & Biases. Experience in automated lending, price sensitivity modelling or CLV is a strong plus. Location and working policy 📍Dublin HQ or London, hybrid. The good stuff 25 days off, plus public holidays. Private healthcare, life insurance, and a pension. Equity, because you should own a piece of what you're building. Generous parental leave for primary and secondary caregivers. 60 days a year to work abroad from wherever you want. By submitting your application, you acknowledge that Wayflyer Limited will process your personal data for the purpose of evaluating your suitability for the role. Such processing is based on the need to take steps prior to entering into a potential employment agreement. To learn more about how we handle your personal data, you can contact our privacy team at privacy@wayflyer.com or review our privacy notice at https://wayflyer.com/privacy-notice.

Elliptic

The impact you will have: As Staff MLOps Engineer, you will define and build Elliptic's Enterprise MLOps platform. Elliptic has growing ML capability across several teams, an established model registry, and a maturing model risk management practice. What is missing is the unified platform layer that ties training, deployment, monitoring, and governance together into a coherent, scalable discipline. You will be responsible for creating that layer. Your platform will serve four distinct internal consumers, each with different needs: * Product Engineering teams building customer-facing models and customer data analytical models, who need reproducible training pipelines, CI/CD for model deployment, and low-latency serving infrastructure * Intelligence Research building frontier intelligence collection, predictive pre-screening models, and behavioural pattern detection, who need rapid experimentation, GPU orchestration, and dataset versioning * InfoSec who own the model registry and model risk management framework today, and need the platform to close execution gaps in audit trails, drift monitoring, and compliance reporting * Operations who own BI, usage prediction, and revenue opportunity signalling, and need scheduled batch inference, BI integration, and pipeline reliability The platform you build must enforce governance with enough rigour to satisfy a regulated financial crime context, while remaining flexible enough to avoid slowing down research teams who need to iterate quickly. This is a role for someone who has built ML infrastructure from the ground up before, who understands that a platform succeeds only when it is adopted, and who is comfortable making build-vs-buy decisions that others will adopt and use for years. What you will do: * Define the target-state MLOps architecture for Elliptic, covering model training pipelines, serving infrastructure, monitoring, feature management, and governance, and produce the architecture decision records that inform investment decisions * Make and document build-vs-buy-vs-stop recommendations with clear cost modelling and trade-off analysis, evaluating vendors, open-source tools, and managed services against Elliptic's constraints (AWS-primary, Databricks ecosystem) * Work with InfoSec to improve the existing model registry and model risk management framework, closing identified gaps in metadata, lineage, approval workflows, and drift/bias detection * Build model training pipelines, CI/CD for ML, and serving infrastructure, working directly with a small group of infrastructure engineers to ship production-grade platform capabilities * Instrument observability across the ML lifecycle: training metrics, serving latency and throughput, data quality, and prediction drift, integrating with Elliptic's existing observability stack * Work directly with data scientists and ML engineers across all four consumer groups to onboard them onto the platform, writing documentation, runbooks, and reference architectures that lower the barrier to self-service You will be a great fit here if you: * Have built MLOps platforms or ML infrastructure from the ground up, and can speak to what worked, what didn't, and why * Have operated in a regulated industry (e.g. compliance, financial) and have hands on experience building ML infrastructure to meet those regulatory demands * Think about ML infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced * Are comfortable operating in ambiguity, making decisions with incomplete information, and creating structure where none exists, while remaining open to changing course when better information arrives * Influence through clarity, evidence, and the quality of your work rather than positional authority. You earn adoption by making the platform genuinely better than the alternative * Care about production engineering quality: you write production-grade code, your systems are tested, observable, documented, and designed for others to operate Our ideal candidate has: * Deep hands-on experience building MLOps platforms, including model registries, feature stores, and ML pipeline orchestration * Working knowledge of model serving patterns: real-time inference, batch prediction, A/B deployment, and deployment strategies * AWS infrastructure experience (ECS/EKS, S3, IAM, networking) and comfort operating in a Databricks ecosystem or equivalent lakehouse architecture * Experience with model monitoring: model evaluation, data drift detection, prediction drift, and performance degradation alerting * A track record of building something from zero and bringing it to a state where others could operate and extend it * Experience in a regulated industry (fintech, financial services, healthcare) where model governance is a compliance requirement * See AI as a core part of how modern engineering gets done, not a passing trend. You actively use it to think faster, prototype faster, and pressure-test your own designs, and you're excited that the bar keeps rising. * Prior experience running formal build-vs-buy evaluations with written decision records Bonus Points for: * Familiarity with model risk management frameworks and the ability to connect governance practices to regulatory expectations * Experience working simultaneously with research-oriented ML teams and production-oriented engineering teams, and understanding how their needs diverge * Infrastructure-as-code fluency (Terraform) * Experience with ClickHouse or similar OLAP engines for low-latency ML feature serving * Blockchain or crypto domain knowledge * Experience working in fraud detection and modelling * Contributions to open-source MLOps tooling JOB BENEFITS > How we work: * Hybrid working and the option to work from almost anywhere for up to 90 days per year * £500 Remote working budget to set up your home office space > Learning & Development: * $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development > Vacation/ Leave: * Holidays: 25 days of annual leave + bank holidays * An extra day for your birthday * Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully-paid leave and leave. > Benefits: * Private Health Insurance - we use Vitality! * Full access to Spill Mental Health Support * Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries * Cycle to Work Scheme