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For Companies At a Glance
- Tasks: Join our analytics team to design experiments and develop predictive models using advanced statistical methods.
- Company: Innovative analytics firm located in Chancery Lane, London, focused on data-driven insights.
- Benefits: Enjoy a hybrid work model with 2 days onsite and 3 days remote, plus competitive daily rates.
- Why this job: Make a real impact by translating complex data into actionable insights while collaborating with diverse teams.
- Qualifications: Advanced degree in a quantitative field and 3+ years of experience in statistical methods required.
- Other info: Ideal for those passionate about causal inference and eager to work in a dynamic environment.
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…)
Contact Detail:
Digitas UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Familiarise yourself with the latest advancements in causal inference and experimental design. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the data science field, especially those who specialise in causal inference. Attend meetups or webinars to gain insights and potentially get referrals that could boost your application.
✨Tip Number 3
Prepare to discuss specific projects where you've applied statistical methods to real data. Be ready to explain your thought process and the impact of your work, as this will demonstrate your hands-on experience.
✨Tip Number 4
Brush up on your programming skills in Python or R, focusing on libraries relevant to causal inference and machine learning. Being able to showcase your technical proficiency can set you apart from other candidates.
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 proficiency in Python or R and any relevant statistical packages you've used.
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 interviews. Brush up on key concepts and be prepared to explain them in simple terms, as you may need to communicate 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
Since proficiency in Python or R is crucial for this position, be ready to talk about your experience with these languages. You might even want to prepare a small coding example or discuss a project where you utilised statistical packages effectively.
✨Communicate Clearly
Given the need to present 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 essential for collaboration.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-life situations. Think of examples where you identified confounding variables or developed robust methodologies for causal effect estimation, and be ready to explain your thought process.
