Senior Customer Experimentation Data Scientist

Senior Customer Experimentation Data Scientist

Full-Time 60000 - 80000 £ / year (est.) No working from home possible
D

At a Glance

  • Tasks: Design and scale experimentation programs to drive business outcomes.
  • Company: Join Datadog, a leader in data analytics and customer success.
  • Benefits: Enjoy competitive pay, flexible work options, and growth opportunities.
  • Other info: Collaborate with diverse teams in a dynamic and innovative environment.
  • Why this job: Make a real impact by guiding customers in data-driven decision making.
  • Qualifications: Experience in data science and strong analytical skills required.

The predicted salary is between 60000 - 80000 £ per year.

Datadog is seeking a Customer Data Scientist to partner with customers in designing and scaling experimentation programs. You will guide experiment design, metric development, and data validation to drive measurable business outcomes.

In this role, you will work cross-functionally with Sales, Product, and Engineering, helping customers build trustworthy, decision-driven experimentation practices and translate analytic insights into product improvements and strategic roadmaps.

#J-18808-Ljbffr

Senior Customer Experimentation Data Scientist employer: Doist

Brink's is an exceptional employer that fosters a collaborative and innovative work culture, empowering employees to drive global commercial strategies in the ATM lifecycle solutions sector. With a strong focus on professional development and growth opportunities, employees are encouraged to expand their skills while contributing to sustainable growth and operational excellence. Located in a dynamic environment, Brink's offers unique advantages such as a diverse team and the chance to build long-term partnerships with global customers, making it a rewarding place to advance your career.

D

Contact Details:

Doist Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior Customer Experimentation Data Scientist

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Doist!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Customer Experimentation Data Scientist at Doist.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Doist.

Apply Directly through Our Website

When you find a suitable opening like Senior Customer Experimentation Data Scientist at Doist, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Senior Customer Experimentation Data Scientist

Experiment Design
Metric Development
Data Validation
Analytical Skills
Cross-Functional Collaboration
Communication Skills
Customer Partnership

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!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Doist, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Doist. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Doist

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Doist!

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.