Job Openings JR-143479 Data Scientist Senior Germany FL

About the job JR-143479 Data Scientist Senior Germany FL

Senior Data Insights Engineer / Data Scientist (Germany)

Role Summary

This is not a classic ML/Data Scientist role.

We are looking for a senior, independent data professional with a strong data engineering + analytics profile. The core of the job is understanding messy healthcare source data (in German) and transforming it into clean, structured, research-grade datasets using SQL, Python, and dbt.

Think Data Engineer with strong analytical instincts or Data Scientist with heavy ETL / data modeling experience.

Top Priorities (Must-Haves)

1. German Language – NON-NEGOTIABLE

  • Must be able to read and understand German medical/clinical data
  • Speaking German is not required
  • Minimum B1, B2 strongly preferred
  • This is critical because patient data is in German and cannot be translated using AI tools

If a candidate cannot confidently read German clinical text do not proceed

2. Seniority & Independence

  • Senior-level IC (can work independently)
  • Comfortable owning data problems end-to-end
  • This role covers responsibilities while a senior team member is on maternity leave

Juniors or profiles needing heavy guidance will not fit

3. Core Skill Set (What Theyll Do Daily)

  • Exploratory Data Analysis (EDA) to understand source data
  • Define business logic to map source data to internal data models
  • Build and maintain ETL / ELT pipelines using:
    • SQL
    • Python (pandas)
    • dbt
  • Evaluate data quality and handle data gaps creatively
  • Collaborate with software engineers (but not pure SWE)

Tech Stack (Experience Expected)

Must have:

  • SQL (joins, aggregations; advanced SQL a plus)
  • Python for data analysis (pandas)
  • Experience with ETL / data pipelines
  • Cloud data environments

Strong plus:

  • dbt
  • Snowflake
  • AWS

Nice to have (NOT required):

  • Healthcare / EHR data experience
  • Oncology familiarity
  • Light exposure to LLMs for data processing

Machine learning modeling is NOT the focus

R, advanced statistics, or deep ML research are NOT required

Ideal Backgrounds

Good fits often come from:

  • Data Engineers with strong analytical / EDA experience
  • Data Scientists who spent most of their time on data transformation & pipelines
  • Analytics Engineers
  • Health tech data professionals (big plus, not mandatory)

Poor Fits (Do NOT Prioritize)

  • Pure ML Engineers
  • Research / outcomes-focused Data Scientists
  • Biostatisticians / pharmacometricians
  • Candidates without German reading skills
  • Junior profiles

Location & Logistics

  • Germany-based preferred, Berlin ideal
  • Hybrid model (2–3 days office) preferred but flexible
  • Location can be flexible if seniority and skills are strong
  • Contract role: 9–12 months
  • Need to start ASAP

Interview Process (For Candidate Expectations)

  1. Customertimes Tech interview with Matheus team (to be confirmed with him who will support)
  2. Client -Screening (project discussion + Python coding)
  3. Client - Technical/analytics interview (EDA, data quality, problem solving)
  4. Client - Behavioral interview

Key Screening Questions for Recruiters

Use these early:

  1. Can you comfortably read and understand German clinical or medical data?
  2. Describe a project where you transformed messy source data into a structured data model.
  3. How much of your recent work was SQL + Python for ETL vs M

Or refer someone