Full-Time Senior Data Scientist
Cint is hiring a remote Full-Time Senior Data Scientist. The career level for this job opening is Experienced and is accepting London, United Kingdom based applicants remotely. Read complete job description before applying.
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As a Senior Data Scientist at Cint, you will play a pivotal role in optimizing the Cint Exchange. Collaborating closely with product and engineering teams, you will deliver data-driven solutions to enhance the performance of existing products, inform the development of new offerings, and deepen our understanding of marketplace dynamics. This role involves advanced data mining and analytics, robust product and data validation, and the development of sophisticated statistical and machine learning-based methodologies.
The ideal candidate will have a strong ability to independently research, develop, and maintain high-impact solutions that align Cint’s capabilities with market needs, directly influencing strategic decisions for the Exchange.
What you will do
- Lead the research, discovery, and full-cycle development of machine learning solutions, including model development, deployment, maintenance, and performance evaluation, ensuring seamless integration into production environments for the Cint Exchange.
- Develop a comprehensive, predictive understanding of marketplace dynamics, including price elasticity, supply/demand balance, and their underlying mechanics within the Cint Exchange, providing deep analytical insights.
- Independently and confidently carry out complex project planning, development, and maintenance end-to-end with minimal supervision.
- Analyze large, diverse datasets to extract impactful insights that can guide Exchange product and pricing strategy.
- Collaborate with cross-functional teams (Product, Engineering, Commercial, Operations, Finance) to design, implement, and test new and existing products, as well as to understand, analyze, explain, and predict the impact of platform algorithms on clients and partners.
- Define, establish, and drive the development of advanced statistical and machine learning methods, setting modeling standards and best practices.
- Conduct exploratory analyses to deepen insights into key metrics and trends, and lead the design and execution of A/B tests and other complex experiments to validate hypotheses for the Exchange.
- Continuously evaluate and validate both internal and external products to ensure Cint's continued success and the health of the Exchange.
- Create clear, effective deliverables that communicate complex insights and recommendations through compelling visualizations and presentations tailored to diverse technical and non-technical audiences.
What we are looking for
- Minimum 5 years of solid working experience in a Data Science capacity, with demonstrated experience in leading projects or initiatives.
- A Master's degree (or equivalent) in Statistics, Quantitative Sciences, Data Science, Operations Research, or a related quantitative field.
- Strong ability to manipulate, analyze, and interpret large, complex datasets independently.
- Deep understanding of advanced statistical techniques and concepts.
- Strong knowledge of various machine learning techniques and their real-world advantages and drawbacks.
- Proven experience applying statistical and modeling techniques to solve complex business problems in real-world scenarios.
- Proficiency in Python (for statistical and ML package tools).
- Proficiency in SQL and working with large-scale databases.
- Comfortable researching and adopting new methods, tools, and techniques, and able to mentor junior colleagues on their application.
Essential Qualities:
- Highly accountable self-starter and quick learner, consistently motivated to deliver high-quality, impactful results.
- Strong data-driven mindset with the ability to translate abstract business requests into actionable analytical initiatives and solutions.
- Excellent written and verbal communication skills.
- Ability to understand the big picture strategic objectives while ensuring meticulous attention to detail in execution.