Jobgether
Staff Data Scientist

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Staff Data Scientist
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist based in United Kingdom.
This is a high-impact opportunity for a senior data science professional to help shape the future of statistical modeling and machine learning in residential real estate valuation.
You will work closely with executive technical leadership to solve complex, open-ended statistical and engineering problems.
The role combines hands-on development with the creation of new valuation methodologies and production-grade analytical systems.
Your work will directly support products and platforms used by consumers, financial stakeholders, and professional appraisers across the United States.
The focus is on interpretable machine learning, statistical modeling, optimization, uncertainty estimation, and reliable production systems rather than deep learning or LLM research.
As the Data Science organization grows, you will also help establish technical standards, mentor future team members, and influence its long-term direction.
This role is ideal for a technically strong, curious problem solver who enjoys turning novel analytical ideas into trusted software.
Accountabilities
- Design, develop, and implement production-quality statistical and machine learning systems for residential property valuation and related analytical applications.
- Partner directly with technical leadership to develop innovative valuation methodologies and solve challenging statistical problems that may not have established solutions.
- Translate research concepts and analytical ideas into scalable, maintainable, production-ready software.
- Design rigorous evaluation frameworks to measure model performance, reliability, accuracy, and robustness, and continuously improve analytical outcomes.
- Develop methodologies for complex use cases, including rare and atypical properties, confidence estimation, and uncertainty quantification.
- Build interpretable machine learning systems that provide transparent and defensible results for consumers and professional users.
- Collaborate closely with software engineering teams to integrate new analytical capabilities into production systems.
- Improve the reliability, maintainability, testing, and overall engineering quality of the machine learning platform.
- Establish technical standards, modeling practices, and engineering best practices for the Data Science function.
- Contribute to technical hiring, mentoring, knowledge sharing, and the development of future Data Science team members.
- Help shape the long-term technical direction and capabilities of the growing Data Science organization.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Requirements
- Strong professional background in machine learning and statistics, with experience applying advanced analytical techniques to complex business or technical problems.
- Demonstrated experience building and deploying production-quality machine learning systems rather than working exclusively in research or experimental environments.
- Excellent Python programming skills and strong software engineering fundamentals.
- Experience with testing, version control, modular architecture, maintainable code, and other practices required for reliable production software.
- Strong understanding of statistical modeling, interpretable machine learning, optimization, and rigorous model evaluation.
- Ability to reason from first principles and solve ambiguous, open-ended problems where established methodologies may not exist.
- Strong analytical and problem-solving skills, with the ability to develop elegant and defensible solutions to difficult statistical and engineering challenges.
- Excellent communication skills, including the ability to explain complex technical concepts clearly to both technical and non-technical stakeholders.
- Collaborative approach to solving challenging technical problems, with the ability to give and receive constructive technical feedback.
- Ability and interest in mentoring colleagues, contributing to technical standards, and helping shape a growing Data Science organization.
- A senior or staff-level mindset, with the technical depth and ownership required to influence modeling, engineering, and organizational direction.


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Benefits
- Compensation range of $225,000–$250,000 per year.
- Opportunity to work on high-impact statistical and machine learning systems used in residential real estate valuation.
- Direct collaboration with senior technical leadership on challenging and open-ended analytical problems.
- Hands-on ownership of production machine learning systems and next-generation valuation methodologies.
- Opportunity to influence technical standards, engineering practices, hiring, and the long-term direction of a growing Data Science organization.
- Significant potential for increased technical leadership responsibilities as the team expands.
- Opportunity to work on interpretable and explainable machine learning systems where accuracy, transparency, and defensibility are critical.
How Jobgether works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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