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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 an 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'll 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. Being able to discuss recent studies or methodologies during your interview can demonstrate your passion and expertise in the field.
✨Tip Number 2
Network with professionals in the data science community, especially those who specialise in causal inference. Attend meetups or webinars where you can connect with potential colleagues and learn about their experiences at StudySmarter.
✨Tip Number 3
Prepare to showcase your programming skills in Python or R by working on relevant projects or case studies. Having a portfolio of your work can help you stand out and provide concrete examples of your capabilities.
✨Tip Number 4
Practice explaining complex statistical concepts in simple terms. Since communication is key in this role, being able to convey your findings clearly to non-technical stakeholders will be crucial during your interview.
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 advanced degree and relevant experience, and be sure to address the specific qualifications listed in the job description.
Showcase Technical Skills: Clearly outline your proficiency in programming languages like Python or R, and any statistical packages you have used. Provide examples of projects where you applied these skills, especially in relation to causal inference and experimental design.
Prepare for Interviews: Be ready to discuss your technical findings and how you would present complex statistical concepts to non-technical stakeholders. Practice explaining your methodologies and results in clear, actionable terms, as this is crucial for the role.
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 approach, so brush up on relevant statistical packages and be ready to discuss how you've used them in your previous work.
✨Communicate Clearly
Since you'll need to present technical findings to stakeholders, practice explaining complex statistical concepts in simple terms. Think of examples where you've successfully communicated your insights to non-technical audiences, as this will show your ability to bridge the gap between data science and business needs.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-life situations. Prepare to discuss how you would design an experiment or control for confounding variables in observational studies. This will help interviewers gauge your practical understanding of experimental design and causal inference.
