Data platforms and analytics foundations
Design and implementation of modern data stacks, self-serve analytics layers, data models, testing, governance, and adoption workflows.
Independent consultancy based in Denmark
Practical data science, data engineering, and platform architecture.
Data Science Solutions helps teams turn scattered data, models, and tooling into reliable systems that people can actually use.
- PhD
- statistics and machine learning
- Senior
- hands-on data and platform engineering
- End-to-end
- from strategy through production delivery
Services
Focused support where data work usually gets stuck.
Engagements can be advisory, hands-on implementation, technical leadership, or a blend. The common thread is getting from ambiguous business needs to dependable production systems.
Data engineering and architecture
Robust ELT/ETL pipelines, cloud architecture, Terraform, CI/CD, observability, and pragmatic decisions about what should be built now versus later.
Machine learning and statistical modelling
Production-oriented ML, causal inference, fraud detection, forecasting, experimental analysis, and clear communication of uncertainty and business impact.
AI-augmented engineering
Practical adoption of agentic coding tools, prompt and workflow design, developer enablement, and integration of LLMs into analyst and engineering processes.
Profile
Led by Phillip B. Colliander.
I am a senior data engineer and data scientist with a PhD in theoretical statistics and machine learning. My work spans platform engineering, ML systems, statistical modelling, executive communication, and hands-on delivery.
Recent work includes building self-serve data platforms, modernising fragmented analytics workflows into tested cloud data stacks, and delivering production ML initiatives with clear business ownership.
LinkedIn profile
Experience
Enterprise platforms, startup data stacks, public-sector analytics, and life-science statistics
Research
ICML, NeurIPS, causal discovery, multiple testing
Stack
Python, R, SQL, Snowflake, dbt, Azure, AWS, GCP, Terraform
Approach
Clear diagnosis, practical build, durable handover.
-
01
Frame the real problem
Clarify business goals, constraints, data reality, and the decision the work must improve.
-
02
Build the right system
Ship simple, tested, maintainable data products and models that fit the team and the operating context.
-
03
Make it land
Document, communicate, train, and embed the workflow so the solution survives beyond the first release.
Contact
Need senior help with a data project?
Send a short note about the problem, the current stack, and the outcome you need. I will reply directly.