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For Companies At a Glance
- Tasks: Uncover insights from customer data and create predictive models to enhance experiences.
- Company: Join a leading analytics recruitment agency with a focus on innovation.
- Benefits: Competitive salary, hybrid working, and opportunities for professional growth.
- Why this job: Make a real impact by transforming data into actionable insights for better customer engagement.
- Qualifications: Experience in data science, machine learning, and strong communication skills required.
- Other info: Collaborative environment with excellent referral schemes and career advancement opportunities.
The predicted salary is between 65000 - 75000 £ per year.
We are seeking for an experienced, passionate and highly motivated Data Scientist who will help discover the information hidden in vast amounts of customer data, and help make data driven decisions to deliver better products, service and relevance to the customers.
THE ROLE
- Customer Science
- Develop and implement predictive models to understand drivers of customer behaviour, including purchase patterns, customer lifetime events and sentiment analysis.
- Create sophisticated customer segmentation using behavioural, transactional, and demographic data.
- Design and build predictive models to enhance personalized customer experiences across all channels.
- Collaborate on design of test & learn methods to measure CRM initiatives' effectiveness.
- Monitor and optimize model performance through continuous improvement cycles.
- Transform analytical solutions into production-ready code.
- Implement models within our existing technology stack.
- Ensure scalability and efficiency of deployed solutions.
- Translate complex analytical findings into clear, actionable insights.
- Create compelling data visualizations to effectively communicate patterns and insights.
- Partner with cross-functional teams to enhance CRM strategies.
- Provide data-driven recommendations to improve customer engagement metrics.
Skills
- Relevant experience in Customer Marketing Data Science including applied statistics and machine learning techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc).
- Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch.
- Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks.
- Experience with ML Ops, including model deployment, monitoring, and retraining pipelines.
- Ability to work cross-functionally with marketing, CRM, and engineering teams.
- Excellent communication skills.
- Experience in a global or multi-regional context is a plus.
If you would like to hear more, please do get in touch.
Data Scientist - Customer Data in West End employer: Datatech
Contact Detail:
Datatech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Customer Data in West End
✨Tip Number 1
Network like a pro! Reach out to people in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects. Use GitHub or a personal website to display your work and make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for interviews by practising common data science questions and case studies. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates like you who are eager to make an impact.
We think you need these skills to ace Data Scientist - Customer Data in West End
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your experience with customer data, predictive modelling, and any relevant technical skills. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data science and how you can contribute to our team. Be sure to mention specific projects or experiences that relate to customer behaviour analysis.
Showcase Your Technical Skills: We love seeing your technical prowess! Make sure to include any relevant programming languages, tools, and methodologies you've used in your previous roles. If you've worked with Python or ML libraries, let us know!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you're keen on joining the StudySmarter family!
How to prepare for a job interview at Datatech
✨Know Your Data Science Stuff
Make sure you brush up on your knowledge of applied statistics and machine learning techniques. Be ready to discuss your experience with predictive models, customer segmentation, and any relevant projects you've worked on. This is your chance to show off your skills!
✨Showcase Your Technical Skills
Familiarise yourself with Python and the ML libraries mentioned in the job description. Be prepared to talk about how you've transformed analytical solutions into production-ready code and your experience with cloud platforms like GCP or AWS. Practical examples will make a strong impression!
✨Communicate Clearly
Since you'll need to translate complex findings into actionable insights, practice explaining your work in simple terms. Think about how you can create compelling data visualisations that tell a story. Clear communication is key, especially when collaborating with cross-functional teams.
✨Prepare for Scenario Questions
Expect questions that assess your problem-solving abilities and how you handle real-world scenarios. Think about past experiences where you monitored and optimised model performance or collaborated on CRM initiatives. Use the STAR method (Situation, Task, Action, Result) to structure your answers effectively.
