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For Companies At a Glance
- Tasks: Join our team to design experiments and analyse data for impactful insights.
- Company: We're an innovative analytics firm based in London, pushing the boundaries of data science.
- Benefits: Enjoy a hybrid work model with flexibility and competitive daily rates.
- Why this job: Make a real impact by applying your skills in a collaborative and dynamic environment.
- Qualifications: Advanced degree in a quantitative field and 3+ years of relevant experience required.
- Other info: Opportunity to work with cutting-edge statistical methods and tools.
Start - ASAP
Duration - 2 to 3 months
HYBRID - 2 days onsite / 3 days remote
Location - Chancery Lane, London
Daily rate - TBC
THE ROLE
We are seeking an exceptional Data Scientist with expertise in causal inference, experimental design, and conformal prediction to join our innovative analytics data science team. In this role, you will leverage advanced statistical methods to extract meaningful insights from complex data, design robust experiments, and develop predictive models with reliable uncertainty quantification.
Core Responsibilities
- Design, implement, and analyse causal inference experiments including natural experiments, and quasi-experimental methods
- Develop and apply conformal prediction frameworks to provide reliable uncertainty estimates for machine learning models
- Identify and control for confounding variables in observational studies
- Create robust statistical methodologies for causal effect estimation
- Collaborate with cross-functional teams to translate business questions into rigorous experimental designs
- Present technical findings to stakeholders in clear, actionable terms
Qualifications
- Advanced degree (MS or PhD) in a quantitative discipline with deep understanding of statistics
- 3+ years of professional experience in applying statistical methods to real data
- Demonstrated expertise in experimental design, including randomized controlled trials and observational study methodologies
- Strong understanding of conformal prediction theory and applications
- Proficiency in programming languages such as Python or R, and relevant statistical packages
- Experience with causal inference frameworks (e.g., potential outcomes, causal graphs, do-calculus)
- Knowledge of modern machine learning techniques and how they intersect with causal reasoning
- Excellent communication skills, with ability to explain complex statistical concepts to non-technical audiences
Preferred Skills
- Experience with heterogeneous treatment effect estimation
- Familiarity with Bayesian methods for causal inference
- Background in epidemiology would be a plus
- Experience working with a causal inference ecosystem (pywhy, causal impact, synth, geolift…)
Data Scientist employer: Digitas UK
Contact Detail:
Digitas UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network with professionals in the data science field, especially those who specialise in causal inference and experimental design. Attend meetups or webinars to connect with potential colleagues and learn about the latest trends and techniques.
✨Tip Number 2
Showcase your expertise in programming languages like Python or R by contributing to open-source projects or creating your own data analysis projects. This will not only enhance your skills but also provide tangible evidence of your capabilities to potential employers.
✨Tip Number 3
Prepare to discuss your experience with causal inference frameworks during interviews. Be ready to explain how you've applied these methods in real-world scenarios, as this will demonstrate your practical knowledge and problem-solving abilities.
✨Tip Number 4
Familiarise yourself with the specific tools and packages mentioned in the job description, such as pywhy and causal impact. Having hands-on experience with these tools can set you apart from other candidates and show your commitment to the role.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in causal inference, experimental design, and conformal prediction. Use specific examples from your past work that demonstrate your expertise in these areas.
Craft a Strong Cover Letter: In your cover letter, explain why you are passionate about data science and how your skills align with the role. Mention your experience with statistical methods and programming languages like Python or R, and how they relate to the job requirements.
Showcase Relevant Projects: If you have worked on projects involving causal inference or machine learning, include them in your application. Describe your role, the methodologies used, and the outcomes achieved to illustrate your capabilities.
Prepare for Technical Questions: Be ready to discuss your understanding of experimental design and causal inference during the interview process. Brush up on key concepts and be prepared to explain them clearly, as you may need to present complex ideas to non-technical stakeholders.
How to prepare for a job interview at Digitas UK
✨Showcase Your Statistical Expertise
Be prepared to discuss your advanced knowledge of statistics and how you've applied it in real-world scenarios. Highlight specific projects where you designed experiments or used causal inference methods, as this will demonstrate your capability to handle the responsibilities of the role.
✨Demonstrate Programming Proficiency
Make sure to mention your experience with programming languages like Python or R. You might be asked to solve a problem or explain your coding process, so brush up on relevant statistical packages and be ready to showcase your technical skills.
✨Communicate Clearly
Since the role requires presenting technical findings to stakeholders, practice explaining complex statistical concepts in simple terms. This will show that you can bridge the gap between technical and non-technical audiences, which is crucial for collaboration.
✨Prepare for Scenario-Based Questions
Expect questions that assess your ability to design experiments and control for confounding variables. Think of examples from your past work where you successfully navigated these challenges, as this will illustrate your practical experience and problem-solving skills.
