twomynds

Data Scientist

• Take end-to-end responsibility for the technical delivery of Data & AI projects—from architecture and implementation through to stable operations
• Ensure quality, on-time delivery, and scalability
• Design and build scalable data pipelines, data warehouses/lakehouses, and cloud platforms
• Deploy models and AI use cases to production and operate them with MLOps, monitoring, and data quality processes
• Develop hands-on with Python, SQL, PySpark, and APIs
• Conduct code reviews and establish best practices and reusable components
• Lead, coach, and develop an interdisciplinary team of Data Scientists and Engineers
• Drive technical standards, automation, and quality assurance
• Make tech stack decisions and develop a technical roadmap
• Collaborate closely with clients and internal teams
• Make technical trade-offs transparent and translate solutions into measurable business value

Data Science Manager

Business Analyst

• Analyse markets, campaigns, customer data, and customer journeys
• Identify opportunities and derive actionable recommendations for Marketing, Sales, and Product
• Contribute to the development and functional structuring of Data & AI use cases, such as personalisation, Next Best Action/Offer, churn prevention, and campaign optimisation
• Prepare reports, analyses, and quantitative data as clear decision-making materials for management
• Contribute to presentations, concepts, and roadmaps for senior management
• Support strategy and conceptual work, as well as the structuring of complex topics
• Support projects with organisation, coordination, and PMO activities
• Prepare meetings and workshops, structure outcomes, and follow up on action items
• Prepare internal and external sessions
• Gather input from specialist departments
• Support clients in becoming progressively more data-driven

Business Analyst – Strategy, Customer Insights

Data Engineer

• Development, testing and operation of robust ETL/ELT data pipelines
• Ingesting, transforming and provisioning data from a wide variety of sources
• Designing and maintaining data models, schemas and structures for data warehouses/lakehouses
• Implementing data validations, quality checks and monitoring
• Building and optimizing cloud data solutions with a focus on performance, scalability and cost
• Working with Infrastructure-as-Code
• Automating recurring data flows, e.g. with Airflow
• Ensuring clean, repeatable deployments with CI/CD
• Providing performant and well-documented datasets for data scientists and analysts
• Supporting the production rollout of models and use cases
• Collaborating with interdisciplinary teams and clients
• Translating business requirements into viable technical solutions

Solutions Engineer

• From PoC to Go-Live: design, develop and implement data, AI and advanced analytics solutions
• Programming & Automating: Python scripts, APIs, small web apps (e.g., Flask, Streamlit, FastAPI)
• Data Engineering & Analytics: build data pipelines, connect databases, process and analyze large datasets
• Machine Learning: build pipelines, connect databases, run analyses and detect patterns
• Visualizing & Presenting: communicate dashboards, reports and insights clearly and concisely
• Stakeholder Management: translate technical content for business teams and coordinate closely with clients

Data Science Consultant, Solutions

• From PoC to Go-Live: design, develop, and implement data, AI, and advanced analytics solutions
• Programming & Automating: Python scripts, APIs, small web apps (e.g., Flask, Streamlit, FastAPI)
• Data Engineering & Analytics: build data pipelines, connect databases, process and analyze large datasets
• Machine Learning: build pipelines, connect databases, perform analyses and detect patterns
• Visualize & Present: communicate dashboards, reports & insights clearly and effectively
• Stakeholder Management: translate technical content for business teams and coordinate closely with clients