Associate scientist Jobs in Zürich

About Advertima Vision Advertima is building the missing infrastructure that brings the power of digital advertising to the physical world.

Today, most advertising inside physical retail stores still operates like the internet did twenty years ago: static screens, no audience intelligence, and almost no measurable performance.

Meanwhile, online advertising evolved into a trillion-dollar ecosystem powered by real-time data, automation, and programmatic infrastructure.

We are changing that.

Advertima has built the technology that transforms physical retail environments into real-time, data-driven media channels.

Using computer vision, edge computing, and distributed AI systems, we translate physical shoppers into anonymized digital audiences that can be measured, targeted, and activated in real time.

This is not a theoretical vision or an incremental improvement.

It is the creation of a completely new global technology standard.

Global retail leaders such as Walmart, Carrefour, FairPrice, SPAR, and EDEKA are deploying our technology across thousands of stores.

Together, we are defining the global standard for In-store Retail Media.

The technical challenge behind this is massive.

Our platform operates across large fleets of edge AI devices deployed globally, processes real-world sensor data in real time, and integrates with complex cloud infrastructure and the global adtech ecosystem.

At the same time, everything we build follows a strict privacy-by-design philosophy.

Our systems operate without processing and storing personal data or images, while still enabling the audience intelligence required for real-time performance media.

This approach, rooted in Switzerland’s strong privacy culture, has become one of the key reasons global retailers trust Advertima to power their next-generation Retail Media infrastructure.

Solving this combination of distributed systems, edge computing, AI inference, privacy-preserving data processing, and global-scale reliability is a hard engineering problem.

That is exactly why we are building our core engineering team in Zurich.

If you enjoy working on complex real-world systems, pushing the boundaries of edge and cloud infrastructure, and helping define a new global technology category, Advertima offers a rare opportunity.

Job Overview We are looking for a Senior Data Scientist to translate real-world shopper and campaign data into analytics use cases that deliver measurable value and drive customer success across the retail media ecosystem.

Our platform generates continuous streams of sensor data from physical retail environments.

This data is combined with campaign delivery data from CMS platforms and external sources such as retailer first-party transactional data to power a growing set of retail media use cases.

Your role is to transform these inputs into analytical frameworks and products that support measurement, optimization, and decision-making for retailers, media agencies, and brand advertisers.

This role sits at the intersection of data science, business consulting, and data engineering collaboration, with the primary goal of delivering Advertima’s value proposition through customer-facing analytics, measurable impact, and customer success. This is an applied data science role focused on connecting data pipelines with operational insights and business decisions, while continuously improving existing analytics use cases and identifying new ones as part of Advertima’s pioneering work in the industry.

In practice, this means: You will translate raw sensor, campaign delivery, and retailer data into structured analytics frameworks mapped to concrete retail media use cases and develop customer-facing analytics products that enable customers to understand and realize the value of Advertima’s technology.

The role involves close collaboration with engineering, product, and client-side teams to operationalize analytics delivery, strengthen monitoring processes and contribute to the development of emerging in-store retail media measurement standards.

You will join a team building analytics that define new standards for how in-store retail media is measured, scaled and translated into business value for customers globally, bringing the performance media capabilities that made online retail media so successful into the in-store environment.

Your role… Translate raw sensor, campaign delivery, and retailer first-party data into analytical frameworks aligned with concrete retail media use cases.

Define metrics and methodologies that structure how raw data is transformed into scalable analytics datasets.

Design and maintain dashboards and analytics products across retailer organizations, media agencies, and brand advertisers.

Write and optimize complex SQL queries on large-scale analytical datasets to support measurement and reporting workflows.

Develop advanced analytics workflows for modeling, validation, experimentation, and insight generation.

Translate analytical outputs into clear business insights supporting operational and commercial decision-making.

Collaborate with client-side data teams to align methodologies and ensure consistency between Advertima analytics and retailer data environments.

Collaborate closely with Data Engineers and Product teams to ensure reliable pipelines powering customer-facing analytics.

Validate analytical outputs and strengthen existing monitoring processes to detect inconsistencies, gaps, or calculation issues across upstream datasets.

Contribute to the evolution of emerging in-store retail media measurement standards in collaboration with industry organizations and partners.

Your profile… BSc/MSc Degree in Data Science, Statistics, Econometrics, Computer Science, or equivalent practical experience.

At least 5 years of experience in applied data science, analytics, or measurement-focused roles.

Experience working in retail, media, advertising, or marketing analytics environments.

Strong SQL skills with experience querying large-scale analytical datasets in distributed query environments (e.g., Trino, BigQuery, Spark SQL, or similar).

Strong Python skills for advanced analytics, data validation, experimentation, and analytical workflow development.

Experience working with modern analytics cloud platforms (e.g., BigQuery, Databricks, or similar).

Strong data visualization skills (e.g., Tableau, or similar).

Experience designing metrics, dashboards, and analytical frameworks for business stakeholders.

Experience translating analytical outputs into actionable recommendations for non-technical audiences.

Experience collaborating with data engineering teams on production analytics datasets and pipeline validation.

Strong communication skills and ability to operate in customer-facing analytical roles.

Fluent in English, both spoken and written.

• Ideally, you also have some experience in any of the following… Retail media campaign measurement and optimization.

Attribution or incrementality measurement methodologies.

Experience working with sensor-based datasets or comparable behavioral data.

• Why Advertima Vision?

Innovative Environment: Join a team combining computer vision, IoT, edge computing, and real-time data pipelines to build enterprise-grade tech that transforms retail and advertising.

We model complex enterprise processes and stakeholder networks, applying AI to drive real-world business impact.

Global Impact: Collaborate with some of the world’s largest retailers and lead the charge in shaping the future of unified omnichannel Retail Media.

Professional Growth: Your role won’t be static.

As Advertima grows, we’ll create new leadership and specialist roles, giving you a chance to lead those new opportunities.

Your work will have a direct impact on our growth trajectory.

Supportive Culture: Be part of a team that values innovation, collaboration, and operational excellence, without the corporate fluff.

Fast-Paced and International Startup Environment: Work in a high-growth scale-up environment where we combine flexibility with scalable processes to meet the complex demands of global Retail and AdTech industries.

Flexible Work Policies: Hybrid work environment with the flexibility to balance your personal and professional life.

Employee Participation Plan: Benefit from our employee participation plan, so your success is directly tied to our company’s success.

Self-Development Budget: Get your own budget for professional development, learning, and growth.

• What You Need for a Cultural Fit Self-Management: You take ownership and responsibility without waiting for direction.

Adaptability and Motivation: You are adaptable, self-motivated, and enjoy working independently as well as collaboratively.

Hands-On Approach: You roll up your sleeves to solve problems and remove obstacles for yourself and your team.

Curiosity and Learning: You have a curious mind and continuously deepen your understanding of tech, the industry, and where our company is heading.

You are willing to ask questions and learn from diverse sources to stay ahead of your industry and specialization.

Tech-Savvy: ClickUp, shared calendars, AI collaboration tools—this is your normal work environment and essential for success in our hybrid work environment.

In-Person Collaboration: You value face-to-face exchanges at the office and at team events, which are important to build strong interpersonal relationships and improve creative work outcomes.

Cross-Functional Teamwork: You’re comfortable collaborating across teams and engaging in open, direct conversations.

Feedback Culture: You give and receive direct feedback naturally; it’s how we get better, together.

You are a Game Changer: You want to shape things, not just manage them.

You seek opportunities to break the status quo and build something better.

• If you are a motivated Data Scientist who enjoys turning complex, multi-source datasets into clear analytical frameworks, driving measurable impact for global retail and advertising customers, we encourage you to apply and help shape the future of in-store retail media measurement.

Please submit your application via this page.

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