At a Glance
- Tasks: Lead customer intelligence projects and develop analytical frameworks for value measurement.
- Company: Join NES Fircroft, a global leader in engineering staffing with 90 years of experience.
- Benefits: Enjoy competitive pay, market-leading benefits, and support for visas and accommodation.
- Other info: Dynamic work environment with opportunities for career growth in various sectors.
- Why this job: Make a real impact by driving insights into customer performance across multiple markets.
- Qualifications: Strong skills in Python, SQL, and experience with Databricks and Spark required.
The predicted salary is between 54000 - 66000 £ per year.
Lead Data Scientist - Customer Intelligence & Value Measurement We are looking for a Lead Data Scientist to drive customer intelligence, incrementality measurement, and value analytics across multiple markets.
This role will lead the development of customer segmentation, customer lifetime value (CLV) models, incrementality methodologies, and value measurement frameworks, providing leadership with robust, defensible insights into customer and commercial performance.
Reconcile customer, transaction, and margin data to create accurate and trusted value estimates.
Develop and maintain analytical frameworks that explain performance drivers and identify discrepancies between data sources.
Build scalable analytical solutions and pipelines using Databricks, Spark, and Python.
Establish strong data validation, audit, and reconciliation practices to support decision-making.
Skills Advanced SQL for large-scale customer, loyalty, payment, and transaction analysis.
Strong Python skills for statistical modelling, cohort analysis, weighting, matching, and reproducible analytics.
Hands-on experience with Databricks, Spark/Py Spark, Delta tables, and automated data workflows.
Proficiency with Git, Azure Dev Ops, and controlled production release processes.
Strong data quality, reconciliation, and audit-trail practices.
Skills Experience in loyalty programmes, CRM analytics, or retail fuel and convenience sectors.
Power BI or enterprise reporting platform expertise.
Production-grade data pipeline design, including incremental processing and Delta MERGE patterns.
Exposure to customer decisioning platforms or business needs analysis (BNA).
With over 90 years' combined experience, NES Fircroft (NES) is proud to be the world's leading engineering staffing provider spanning the Oil & Gas, Power & Renewables, Chemicals, Construction & Infrastructure, Life Sciences, Mining and Manufacturing sectors worldwide.
With more than 80 offices in 45 countries, we are able to provide our clients with the engineering and technical expertise they need, wherever and whenever it is needed.
We offer contractors far more than a traditional recruitment service, supporting with everything from securing visas and work permits, to providing market-leading benefits packages and accommodation, ensuring they are safely and compliantly able to support our clients.
Data Scientist (Python, ML, SQL) in London employer: NES Fircroft
At NES Fircroft, we pride ourselves on being an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration in the power technology sector. Our commitment to employee growth is evident through comprehensive training programs and hands-on support, ensuring that our team members thrive in their roles while contributing to cutting-edge projects. Located in a vibrant area, we provide a unique opportunity to work with industry leaders, enhancing your career in a supportive and forward-thinking culture.
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We think this is how you could land Data Scientist (Python, ML, SQL) in London
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We think you need these skills to ace Data Scientist (Python, ML, SQL) 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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