At a Glance
- Tasks: Monitor revenue, investigate anomalies, and maintain production models in a dynamic environment.
- Company: Join an award-winning global SaaS company with a collaborative culture.
- Benefits: Enjoy hybrid working, free breakfast, and a supportive work environment.
- Other info: Opportunity for contract extension and career growth in a global organisation.
- Why this job: Make a real impact by driving analytics and supporting business growth.
- Qualifications: 5+ years in data science, strong SQL, Python or R skills required.
The predicted salary is between 60750 - 74250 £ per year.
Our award-winning global SaaS client is seeking an experienced, hands-on Senior Data Scientist covering International (UK and CA) run-the-business analytics and data science. You will be the sole senior owner, keeping revenue visibility, regulatory reporting, and the current model suite running. This role is a 6 Month contract, Hybrid working – potential to extend. You will work closely with product, marketing, engineering, business development, compliance, and member support. This role suits an operator who takes over existing BAU and runs it end to end with minimal hands-on support, staying reliable under real deadlines.
Responsibilities
- Monitor the International revenue baseline across the UK and CA.
- Investigate anomalies and give early warning on revenue movement.
- Support partner monitoring and escalations: new offer setup, lookalike and recommender admin, partner click-report fixes, and partner ranking changes.
- Run email campaign and performance monitoring: anomaly investigation, re-targeting, send-failure triage, and churn-driver analysis.
- Support experimentation and testing: experiment monitoring, reads, and promotion calls.
- Represent analytics in forums and business reviews, and translate technical detail for business partners.
- Maintain the current suite of production models: scheduled refreshes, retraining, and redeploys. Keep models healthy without rebuilding them.
- Hand over models, pipelines, and reporting with monitoring and documentation to the permanent team at contract close.
Skills
- 5+ years of experience in data science and analytics, including running production models and BAU analytics.
- Strong SQL and Python or R. Comfortable reading and maintaining an existing codebase and pipelines.
- Hands-on experience maintaining production ML models: refresh, retrain, monitor, and troubleshoot, rather than building from scratch.
- Working knowledge of Looker, Airflow and Google Cloud.
- Advanced degree in a quantitative discipline (Computer Science, Mathematics, Statistics, Physics, or similar), or equivalent experience.
Benefits
- Hybrid working (3 days in the office).
- Award winning work environment.
- Global Organisation.
- Free breakfast.
Data Specialist in London employer: Executive Facilities Careers
Join an award-winning law firm that values your expertise and offers a dynamic work environment in the heart of the real estate sector. With a strong focus on employee growth, you will have access to comprehensive benefits, flexible working arrangements, and the opportunity to make a significant impact on the firm's success while enjoying a competitive salary and generous bonuses. This is not just a job; it's a chance to thrive in a supportive culture that encourages professional development and collaboration.
Contact Details:
Executive Facilities Careers Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Specialist in London
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We think you need these skills to ace Data Specialist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
