Data Scientist - AI & Experimentation

United StatesRemoteFull-timemid

Data ScientistData

0 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

Develop, validate, and deploy statistical and predictive models that inform pricing, job matching, and growth decisions. Build and improve LLM-powered product features,

Requirements Summary

The ideal candidate combines strong statistical foundations with practical machine learning and modern AI experience. You should be comfortable working independently with data,

Technical Tools

Data ScientistData

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.

You will lead data science initiatives from problem definition and modeling through experimentation, deployment, and performance monitoring. Your responsibilities will include:

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

~2 min read

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.