Data Scientist

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🇬🇧 United Kingdom – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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CEOX

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2019

🤝 B2B

📚 Education

🎯 Recruiter

B2B • Education • Recruitment

CEOX is a highly vetted, mission-driven community and leadership network that advances women into the C-suite and CEO roles by connecting executive women with peer cohorts, leadership programs (e. g. , Ready to Rise), executive coaching, board and career opportunities, recruiting services, and sponsorship/partnership options. It supports members with confidential executive cohorts, candidate lists and job board access for recruiters, premium investment resources, and exclusive peer forums to reduce isolation at the top and increase access to leadership opportunities.

📋 Description

• Translate requirements from analysts and business stakeholders into problems that can be addressed through statistical or machine learning approaches. • Design, build, and validate time series forecasting models using both statistical methods (e.g. ARIMA) and machine learning approaches (e.g. random forest, deep learning). • Apply supervised learning techniques to relevant business problems, with strategies such as active learning used where appropriate to improve model performance. • Carry out feature engineering to support both forecasting and supervised learning models. • Evaluate the outputs of large language models, applying methods such as prompt engineering, retrieval-augmented generation (RAG), and metadata to support knowledge base accuracy. • Communicate modelling approaches and assumptions to stakeholders before development, and present results and their implications iteratively as work progresses. • Develop and maintain Python and SQL code under version control, working to DevOps practices for testing, deployment, and collaboration; document modelling decisions, assumptions, and evaluation criteria to organisational standards.

🎯 Requirements

• Demonstrated experience in time series analysis, including machine learning approaches (feature engineering, deep learning, random forest) and statistical approaches to forecasting (e.g. ARIMA). • Demonstrated experience in supervised learning approaches, with experience of strategies such as active learning considered an advantage. • Proven ability to translate requirements from analysts into a problem that can be solved through modelling, and to communicate modelling approaches and outcomes clearly before, during, and after development. • Experience evaluating the outputs of large language models, including the use of approaches such as prompt engineering and retrieval-augmented generation (RAG), and the use of metadata to support a knowledge base. • Strong working knowledge of Python and SQL. • Essential experience with version control and working within a DevOps approach to development and deployment. • Ability to work with both structured and unstructured data sources to support model development.

🏖️ Benefits

• Competitive Compensation Package