Business Operations (Intern / Working Student)

Adaptyv Bio

Adaptyv is building an automated lab that lets AI agents run biology experiments. We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world. We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today. Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development. We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation. ABOUT THE ROLE This is an internship for an exceptional student or early-career builder — your shot to do the most serious work of your life so far, on a small team where interns own real problems. Building an automated lab throws off an endless stream of problems, and they don't respect job titles. One week it's a gnarly data pipeline, the next a misbehaving instrument, a model that needs evaluating, or a supplier bottleneck blocking production. We'll point you at the ones that fit you, give you a senior person to learn from, and expect you to make a real dent. The goal is simple: create obvious, measurable value. Do that, and this doesn't have to end as an internship — if you're good, you stay.   WHAT YOU MIGHT WORK ON Depending on where you're strongest and where we need you, any of: * Software / ML — internal tools, data pipelines, automation scripts, model evaluation, agents. * Lab automation — getting instruments talking, debugging work cells, building hardware glue. * Biology — hands-on in the lab, supporting and improving production workflows. * Operations / logistics — untangling supplier, inventory, or process bottlenecks. You'll go deep on one or two of these — not all. We care that you're excited the list is this wide and that you learn new domains fast.   WHAT WE'RE LOOKING FOR * A student or recent grad — or someone early in their path who hasn't found the right box yet. This is explicitly an early-career role. * Show us something you've built. A project, repo, paper, hack, side business, competition win — proof you make things and don't wait for permission. A strong application leads with this, not with your transcript. * Quick on your feet. You walk into an unfamiliar problem, work out what matters, and start making progress on your own. * A fast learner who's genuinely generalist. You pick up new domains quickly and aren't precious about which kind of problem you work on. * AI-native. It's 2026 — you build with coding agents like Claude Code as a default and get far more done because of it. * High energy, high agency. You see what needs doing and do it. When you apply, lead with the single most impressive thing you've built so far and a link to it.   DETAILS * Location: Lausanne, Switzerland (on-site) * Type: Internship * Start date: Flexible We review applications on a rolling basis. Application deadline We are reviewing applicants on a rolling basis.

Artefact

WHO IS ARTEFACT? Artefact is a next-generation strategy and data consulting firm. We sit at the intersection of top-tier strategy consulting and deep technical expertise. Our mission is to transform data into a competitive advantage for the world’s largest brands. In our Romandie office, we don't just "process" data—we use it to solve high-stakes business puzzles. We are looking for a Senior Consultant who can bridge the gap between complex data ecosystems and executive-level decision-making. ---------------------------------------------------------------------------------------------------------------------------------- THE MISSION STATEMENT: TRANSLATING DATA INTO STRATEGY As a Senior Analytics Consultant, you are the primary link between a major global client’s business goals and their technical infrastructure. This role isn't about building heavy infrastructure from scratch; it’s about Analytics Engineering—architecting the logic that turns raw information into business intelligence. CORE RESPONSIBILITIES * Strategic Stakeholder Management: Lead workshops with international business leads to define KPIs and translate vague business questions into precise technical requirements. * Analytics Engineering (dbt & BigQuery): Design and maintain high-quality data models. You will own the "logic layer," ensuring that data is transformed, historized, and optimized for business use cases. * Storytelling & Visualization: Architect the Looker (LookML) environment. You will ensure that dashboards aren't just "pretty," but are actionable tools that global markets rely on daily. * Process Leadership: Act as the guardian of data quality. You will define documentation standards, conduct audits, and ensure the long-term scalability of the analytics solution. * Proactive Advisory: As a Senior member, you are expected to challenge the status quo. You will identify opportunities where data can drive more value and propose roadmap enhancements to the client. ---------------------------------------------------------------------------------------------------------------------------------- CANDIDATE PROFILE: THE "CONSULTANT MINDSET" We are looking for a "Translator." You should be someone who enjoys the nuance of business operations just as much as a clean SQL query. * The Mindset: You are a consultant first. You understand that tech is a means to an end. You are comfortable presenting to senior stakeholders and can explain complex data flows in simple, commercial terms. * Technical Proficiency: * Expert-level SQL and significant experience with dbt. * Hands-on experience with the Google Cloud Platform (BigQuery). * Professional mastery of Looker and LookML. * Business Acumen: You have a solid grasp of how global corporations operate (supply chain, distribution, or commercial data). Experience with Anaplan logic is a major plus. * Experience: 3+ years in data analytics, business intelligence, or consulting. You have a track record of owning projects from requirements gathering to final delivery. * Languages: Fluency in English and French are mandatory. ---------------------------------------------------------------------------------------------------------------------------------- WHY JOIN ARTEFACT SWITZERLAND? * Senior Impact: You will have high visibility within the firm and with the client. You aren't a cog in a machine; you are an architect of solutions. * Modern Analytics Stack: We focus on the "best of breed" tools (GCP, dbt, Looker), allowing you to spend more time on insights and less time on technical "plumbing." * Entrepreneurial Culture: We are a fast-growing team in Switzerland. You will help shape the culture and the way we deliver projects. * Focus on Value: We prioritize tangible business impact over technical vanity projects. This position is located in the Geneva area.

Adaptyv Bio

Adaptyv is building an automated lab that lets AI agents run biology experiments. We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world. We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today. Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development. We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation. ABOUT THE ROLE We already use AI across every part of the company — business operations automation, data analysis and reporting, AI-driven review of customer experiment data, agentic workflows for lab scheduling and customer communication, and a lab-wide assistant the team leans on. The capabilities largely exist. What's missing is someone whose entire job is taking what we've already built and making it successful: wrapped, installable, wired into the tools people use every day, and turned into the default way the company works. This is an internal-facing role focused on process optimization. You won't spend most of your time inventing new features — you'll take the capabilities that already exist across LabOS, our internal APIs, and our AI systems and make them genuinely easy to access, reliable, and adopted. The win condition is the rest of the company moving faster because the thing you built became the obvious option. In a given week, that might mean: * Wrapping our internal APIs (lab orchestration, instrument automation, experiment data) into clean, installable SDKs and MCP servers so agents and teammates can plug into them in minutes instead of reverse-engineering endpoints * Building and improving our lab-wide assistant — its system prompt, its skills, and the integrations that let it actually act through our APIs rather than just talk about it * Turning manual business processes into agents and workflows: procurement alerts, invoice reconciliation, revenue and reporting pipelines, customer update drafting * Pulling together experiment, commercial, and operational data to answer questions and surface insights the team would otherwise miss — the analysis nobody has time to do by hand * Taking a powerful-but-buried capability and making it the new default — packaging it, documenting it, putting it where people already work, and making sure it actually gets used * Setting up evals, observability, and monitoring so the systems you build and the models you use perform as expected and catch regressions automatically This is not an ML research role. You won't be training protein language models or publishing papers. You'll be building the applied AI systems, internal tooling, and glue that make a small, fast-moving team operate like one ten times its size. WHAT WE'RE LOOKING FOR * Strong software engineering fundamentals. You build production systems, not notebooks. TypeScript or Python at minimum — but ideally language doesn't matter to you, and you're comfortable in both. * Deep hands-on experience with LLMs and agentic patterns — knowing when and how to apply function calling, tool use, multi-step workflows, MCP, and retrieval to create value. You've shipped real systems, not wrappers around chat completions. * A platform instinct. You like taking something that works for one person and turning it into something the whole team can install and use — good defaults, clean interfaces, and docs that mean nobody has to ask you how it works. * Process-to-agent instinct. You look at a manual business process and immediately see where an agent or workflow would do it better. Then you build it, test it, and hand it over by Friday. * Fluent with data. You can dive into a messy database or spreadsheet, pull the right numbers, and turn them into an answer, a dashboard, or an automated report. SQL and a notebook/BI habit are second nature. * Comfortable working across every team. You'll talk to lab scientists about data review, to ops about procurement, to the commercial team about customer workflows. The AI touches everything. * Ships fast, owns the result. You prototype in a day, get feedback, iterate. And you're responsible for everything your agents produce — shipping fast does not mean dumping slop on the rest of the team. Your systems are maintainable and you can strike the right tradeoff between moving fast now and moving fast in the future. * Curious about biology. No background required, but you should find it genuinely interesting that we're building infrastructure for AI to run experiments in the physical world. Application deadline We are reviewing applicants on a rolling basis.