Job Description
Company description At Digitas, we harness the power of connection to make positive impact everyday. We have a relentless focus on creating connections to help our clients’ businesses grow, connecting diverse people, ideas and expertise in innovative and exciting ways. We are making positive impact with our amazing clients, through our capabilities in Consulting, Products & Platforms, Customer Engagement and Digital Media. Part of Publicis Groupe, and a Leader in Gartner’s Magic Quadrant for Global Marketing Agencies, we’re proud to work with some of the world’s leading brands. Digitas. Experience the power of connection. Our Commitment Digitas is an equal opportunities employer and welcomes applications from all sections of society and does not discriminate on grounds of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation or gender identity. Overview Data Science Lead – Digitas We are hiring a Data Science Lead to help shape how we turn complex data into meaningful impact for our clients and teams. At Digitas, you’ll work at the intersection of analytics, experimentation and innovation—bringing clarity to challenging questions and driving confident decisions. This is a role where your expertise will directly influence how we design, measure and optimise real-world outcomes. You’ll join a collaborative environment where ideas are valued, and curiosity is encouraged. Responsibilities What you'll be doing: Design and deliver rigorous causal inference studies, including natural and quasi-experiments that inform business decisions Build and apply frameworks that quantify uncertainty in predictive models, enabling confident decision-making Identify, assess and manage confounding variables in observational datasets Develop robust statistical approaches to estimate causal impact across a range of client challenges Translate business questions into structured experimental designs and analytical approaches Partner with cross-functional teams to embed data science into product, marketing and consulting initiatives Communicate findings clearly, turning complex analysis into actionable insights for technical and non-technical audiences Qualifications Skills we're looking for: Strong grounding in statistics and quantitative methods, supported by relevant academic or professional experience Hands-on experience applying experimental design and causal inference techniques to real-world data Understanding of modern approaches to uncertainty estimation in machine learning (e.g. conformal prediction) Proficiency in Python, R, or similar programming languages used for data analysis Familiarity with causal frameworks such as potential outcomes or causal graphs Working knowledge of machine learning and how it integrates with causal reasoning Clear and confident communicator, able to explain complex ideas in a simple and engaging way Additional information Digitas offers a wide range of benefits to support our employees. Full details are shared when you join, but highlights include core benefits such as Pension, Life Assurance, and Private Medical cover, alongside enhanced policies like Reflection Days and Shared Parental Leave. You’ll also have access to a range of additional initiatives, including: 📖 Please check out the Publicis Career Page which showcases our Inclusive Benefits and our EAG’s (Employee Action Groups). Publicis Groupe works primarily from the office or our clients' office four days per week. At Digitas, we are proud to be an equal opportunities employer. We welcome and encourage applications from people of all backgrounds, and do not discriminate on the basis of race, ethnicity, nationality, religion or belief, disability, age, citizenship, relationship status, sexual orientation, gender identity, or any other protected characteristic. We are committed to providing a fair, accessible, and inclusive recruitment process. If you have any access needs - for example, related to disability, neurodivergence, or a health condition - please let us know. We’ll work with you to ensure the process works for you. Sharing this information will never impact your application. Guided by our values, we listen with empathy, uplift each other, take responsibility, and embrace change - building a culture where everyone feels seen, respected, and genuinely included. #LI-PH1
Skills we're looking for: Strong grounding in statistics and quantitative methods, supported by relevant academic or professional experience Hands-on experience applying experimental design and causal inference techniques to real-world data Understanding of modern approaches to uncertainty estimation in machine learning (e.g. conformal prediction) Proficiency in Python, R, or similar programming languages used for data analysis Familiarity with causal frameworks such as potential outcomes or causal graphs Working knowledge of machine learning and how it integrates with causal reasoning Clear and confident communicator, able to explain complex ideas in a simple and engaging way
What you'll be doing: Design and deliver rigorous causal inference studies, including natural and quasi-experiments that inform business decisions Build and apply frameworks that quantify uncertainty in predictive models, enabling confident decision-making Identify, assess and manage confounding variables in observational datasets Develop robust statistical approaches to estimate causal impact across a range of client challenges Translate business questions into structured experimental designs and analytical approaches Partner with cross-functional teams to embed data science into product, marketing and consulting initiatives Communicate findings clearly, turning complex analysis into actionable insights for technical and non-technical audiences