3 days ago

Mid Data Scientist

Jobgether

Netherlands

3 days ago

Apply

Mid Data Scientist

Mid Data Scientist role based in the Netherlands, offered on behalf of a partner company, focused on turning complex data into actionable insights and measurable business value. The position involves building and deploying machine learning models, statistical analysis, data visualization, and MLOps practices, requiring 3–5 years of data science experience and strong Python skills.

Core AIHybridFull-timeMid LevelPythonNumPy

Mid Data Scientist

Apply

Core AIHybridFull-timeMid LevelPython

Salary

Not specified

Work Location

Netherlands, NL

Work Model

Hybrid; 100% remote work opportunities with flexibility to work from preferred location

Experience Required

5 years

Employment Type

Full-time

Experience Level

Mid: 3–5 years

Core Qualifications

Technical (Must-have)

PythonNumPypandasscikit-learnPyTorchTensorFlowSQLMLflowWeights & BiasesDatabricks MLMatplotlibSeabornPlotlyPower BITableau

Soft Skills

Analytical thinkingProblem-solvingCommunicationCollaborationOwnershipAdaptabilityProactive communication

Preferred Qualifications

Technical (Nice-to-have)

Azure MLAWS SageMakerGoogle Cloud Vertex AI

Key Responsibilities

•Conduct exploratory data analysis to identify patterns, trends, anomalies, and opportunities.
•Perform feature engineering and prepare high-quality datasets for analytical and machine learning use cases.
•Build, train, tune, and validate supervised and unsupervised machine learning models.
•Apply statistical and probabilistic methods, including hypothesis testing, inference, and distribution analysis.
•Define appropriate evaluation metrics and validation strategies, including cross-validation and overfitting analysis.
•Use experimentation and model management tools such as MLflow, Weights & Biases, or Databricks ML.
•Analyze and query data using SQL.
•Develop clear and informative data visualizations using Matplotlib, Seaborn, Plotly, and BI platforms such as Power BI or Tableau.
•Apply MLOps fundamentals, including model versioning, model registries, and deployment lifecycle practices.
•Work with cloud-based machine learning platforms such as Azure ML, AWS SageMaker, or Google Cloud Vertex AI.
•Communicate analytical findings and technical insights clearly to both technical and non-technical stakeholders.
•Collaborate with Data teams to integrate analytical solutions effectively across projects.
•Take ownership of model quality, reliability, and the overall analytical lifecycle.
•Adapt analytical approaches and models to changing datasets, requirements, and project objectives.
•Proactively identify problems and communicate solutions in a structured, value-oriented manner.

Data ScienceMachine LearningPythonSQLMLOpsHybridNetherlandsMid LevelInternet Marketplace PlatformsFull-time

Key Responsibilities

•Conduct exploratory data analysis to identify patterns, trends, anomalies, and opportunities.

•Perform feature engineering and prepare high-quality datasets for analytical and machine learning use cases.

•Build, train, tune, and validate supervised and unsupervised machine learning models.

•Apply statistical and probabilistic methods, including hypothesis testing, inference, and distribution analysis.

•Define appropriate evaluation metrics and validation strategies, including cross-validation and overfitting analysis.

•Use experimentation and model management tools such as MLflow, Weights & Biases, or Databricks ML.

•Analyze and query data using SQL.

•Develop clear and informative data visualizations using Matplotlib, Seaborn, Plotly, and BI platforms such as Power BI or Tableau.

•Apply MLOps fundamentals, including model versioning, model registries, and deployment lifecycle practices.

•Work with cloud-based machine learning platforms such as Azure ML, AWS SageMaker, or Google Cloud Vertex AI.

•Communicate analytical findings and technical insights clearly to both technical and non-technical stakeholders.

•Collaborate with Data teams to integrate analytical solutions effectively across projects.

•Take ownership of model quality, reliability, and the overall analytical lifecycle.

•Adapt analytical approaches and models to changing datasets, requirements, and project objectives.

•Proactively identify problems and communicate solutions in a structured, value-oriented manner.