Data Scientist - AI & Experimentation

Jobgether; United Kingdom; Remote; GBP 35,000 - 63,000; Full time

Benefits offered by this job

Remote work

Office Berlin-Mitte style access

Open and collaborative culture

Professional development

Drinks at the office

Job summary

Jobgether is seeking a Data Scientist - AI & Experimentation to work remotely in the United Kingdom on a data-driven healthcare recruitment platform. You will build predictive models, enhance AI-powered features, and run experiments to influence product strategy and business outcomes.

You will collaborate with product, engineering, and marketing to translate complex findings into actionable decisions. The role offers end-to-end ownership of data initiatives in a fast-moving environment.

Qualifications

• Strong knowledge of statistics with ability to justify methods and interpret results.
• Advanced Python, pandas, scikit-learn, NumPy and strong SQL skills.
• Experience applying data science to real-world business problems.
• Hands-on ML/AI development from concept to production.

Responsibilities

• Develop, validate, and deploy statistical and predictive models for pricing, matching, and growth.
• Build LLM-powered features including RAG pipelines and AI applications.
• Design prompt engineering and evaluation workflows for reliability and quality.
• Design experiments including A/B tests to support data-driven decisions.
• Analyze funnels and user journeys to improve healthcare professional matching.
• Collaborate with marketing to optimize audience targeting and attribution.
• Define KPIs and build dashboards with AWS QuickSight and other tools.
• Track model performance, experiments, and business impact.
• Document projects and communicate progress via Jira and similar tools.
• Present findings to stakeholders across product, engineering, and marketing.
• Follow through on recommendations to measure impact after deployment.

Skills

Statistics

Python

SQL

Machine Learning

LLMs

A/B testing

Experiment design

Data analysis

Jira

Communication

Education

Mathematics / Statistics / Physics / CS degree

Tools

Python data stack

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist - AI & Experimentation based remotely in United Kingdom.

We are looking for a data-driven Data Scientist to combine statistical modeling, machine learning, and experimentation to improve a digital job-matching platform in the healthcare recruitment sector. In this role, you will develop predictive models, enhance AI-powered features, and design experiments that directly influence product strategy and business performance. You will work with large datasets to understand user behavior, optimize matching between supply and demand, and uncover opportunities for growth. From LLM-powered applications and Retrieval-Augmented Generation (RAG) systems to A/B testing and marketing analytics, your work will move beyond analysis into practical implementation. Collaborating with product, engineering, and marketing teams, you will turn complex findings into measurable business outcomes. This is an opportunity to take end-to-end ownership of data initiatives in a collaborative, fast-moving environment where analytical rigor and real-world impact matter.

Accountabilities

• Develop, validate, and deploy statistical and predictive models that inform pricing, job matching, and growth decisions.
• Build and improve LLM-powered product features, including Retrieval-Augmented Generation (RAG) pipelines and applications of emerging AI technologies.
• Design prompt engineering and evaluation workflows to assess AI output quality, monitor reliability, and identify performance regressions before they affect users.
• Translate business questions into testable hypotheses and design experiments, including A/B tests, that support clear, evidence-based decisions.
• Analyze conversion funnels and user journeys to identify drop-off points, uncover friction, and improve matching between healthcare professionals and employers.
• Partner with performance marketing teams to optimize audience targeting, attribution, and campaign efficiency.
• Define meaningful key performance indicators (KPIs) and build dashboards and monitoring systems using tools such as AWS QuickSight.
• Track model performance, experiment outcomes, and business impact to ensure data initiatives deliver measurable results.
• Document projects, prioritize tasks, and communicate progress transparently through Jira and other collaborative workflows.
• Present findings and actionable recommendations to stakeholders across product, engineering, and marketing.
• Follow through on recommendations to support implementation and measure their impact after deployment.

Requirements

The ideal candidate combines strong statistical foundations with practical machine learning and modern AI experience. You should be comfortable working independently with data, selecting appropriate analytical methods, and translating technical results into business decisions. The key qualifications include:

• Strong knowledge of statistics, including the ability to select appropriate methods, justify assumptions, and interpret results critically.
• Advanced proficiency in Python, particularly pandas, scikit-learn, and NumPy, as well as strong SQL skills.
• Demonstrated experience applying statistical analysis and data science techniques to real-world business problems.
• Hands‑on experience taking machine learning models and modern AI solutions from initial concept through implementation and practical use.
• Practical experience with LLMs and RAG systems in production or near‑production environments, including prompt engineering, retrieval quality, and output evaluation.
• Solid understanding of A/B testing, including sample size estimation, statistical significance, common experimental pitfalls, and situations where experimentation may not be appropriate.
• Working knowledge of performance marketing metrics and concepts, including Customer Acquisition Cost (CAC), Return on Ad Spend (ROAS), and marketing attribution.
• Project experience in at least one of the following areas: anomaly detection, trend analysis, marketing mix modeling, or multi-touch attribution.
• Experience in e‑commerce, online marketplaces, or other platform-based businesses, ideally involving supply-and-demand dynamics.
• Strong analytical thinking, problem-solving abilities, and attention to data quality.
• Ability to communicate complex findings clearly, make practical recommendations, and collaborate effectively with cross-functional stakeholders.
• Experience documenting work, managing priorities, and tracking projects using Jira or similar tools.
• A degree in mathematics, statistics, physics, computer science, or another quantitative discipline is an advantage.

Benefits

• Annual salary ranging from €42,000 to €75,000.
• Permanent employment opportunity.
• Remote work arrangement, with a home-office option.
• Flat organizational structure, short decision-making processes, and a start-up mindset.
• Open and collaborative company culture with supportive, communicative colleagues.
• Regular feedback conversations to encourage professional development.
• Access to a centrally located office in Berlin-Mitte, with an open-plan working environment.
• Complimentary drinks at the office.
• Opportunity to work on practical AI, machine learning, and experimentation projects with a direct impact on product and business performance.

We appreciate your interest and wish you the best!

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.