Data scientist Jobs in Bielefeld

The Role

You’ll own the whole lifecycle, analyze → design → implement → release → operate, applying systems thinking to model dependencies and identify leverage points in the system, making clear engineering decisions to turn ambiguity into shippable software.

You’ll collaborate directly with customers, clients, and teammates to clarify problems, present designs and trade-offs in writing and demos, and communicate crisply throughout execution.

What You’ll Do

• Build with AI: use generative tools, evals, and our components; contribute new patterns back into our blueprint.

• Engineer solutions and make decisions: evaluate trade-offs, pick patterns, make the architecture designs, develop and commit.

• Pair daily with product and design; turn ambiguous problems into shippable increments.

• Build scalable services, APIs, data pipelines, and frontends; instrument, observe, and optimize them in prod.

• Own the solution. Reliability, scalability, performance, availability.

• Work directly with the clients: clarify requirements, validate solutions, and close the loop. Fast.

• Document clearly and present effectively to technical and non-technical stakeholders.

Required Skills

• Senior-level expertise in designing, engineering, and implementing large-scale solutions. Node.js + TypeScript, React/Next.js, PostgreSQL; Python; Rust or anything else of your taste

• Data and systems fundamentals: schema design, pragmatic caching, event-driven architectures (Kafka/Redis)

• Cloud and edge: AWS or Azure; Cloudflare (Workers/Durable Objects/KV) experience is excellent

• Systems thinking mindset; comfortable writing specs, design docs, ADRs, etc.

• Excellent written and verbal communication; ability to analyze, design, solution, and present

• Experience building internal platforms or templates that speed up other engineers

Typical Stack

• Languages / Frameworks: Node.js, TypeScript, Python, Rust or Ruby

• Data: PostgreSQL, ClickHouse, Redis, Airflow

• Messaging/Streaming/Workflows: Kafka, Temporal

• Cloud / Edge: GCP, Azure or AWS is OK but our love goes to Cloudflare (Workers, Durable Objects, KV, R2, etc.)

• Infra/Tooling: Docker, GitHub Actions, IaC (Terraform)

Perks and Benefits

• Competitive salary + bonus; equity where applicable.

• Up to 30 days PTO; public holidays (location-based); high-end laptop + tools; learning budget; extensive AI tooling.

• Fully remote from the EU, the UK or the US, with occasional travel.

FAQ

Remote?

Yes, fully remote from anywhere in Germany after a successfull onboarding.

Visa?

Case by case.

Tech stack?

Whatever works; we prefer JS/TS + Python (see stack above). Rust or Ruby sometimes happens.

AI tools?

Extensively.

Work-life?

Work hard. Rest well. No weekends unless prod is on fire.

Growth path?

Build products → Lead practices → Build the company.

Who shouldn’t apply?

Process evangelists, architecture astronauts, meeting enthusiasts, or anyone needing constant supervision.

Equal Opportunity

We’re an equal opportunity employer. We evaluate only on the ability to ship great software.

AIFANO helps businesses identify where AI creates genuine value, rethink their processes, and develop production-ready solutions. We do not view AI merely as an add-on tool patched onto existing workflows. Instead, we ask a more fundamental question: How should a business operate if AI is integrated from the very beginning? This is the AI-first approach.

From AI strategy and transformation roadmaps to the redesign of business processes and the deployment of production-ready AI systems, we bridge the gap between strategy, transformation, and engineering.

Together with our clients, we develop new ways of working where humans, AI agents, and existing enterprise systems collaborate seamlessly. This creates more than just isolated automations; it builds the scalable infrastructure required for an AI-native enterprise.

Our core focus spans four key areas: AI Strategy, Process Transformation, AI Engineering, and AI Products. This encompasses agentic workflows, digital workers, intelligent knowledge systems, and custom AI-native software.

Our ambition: fewer AI pilots stuck in the experimental phase, and more agentic applications delivering measurable value in day-to-day operations.

From AI experimentation to production.