Mid Data Scientist

Accountabilities:

As a Mid Data Scientist, you will own key parts of the analytical and machine learning lifecycle, from exploring data and engineering features to developing, evaluating, and supporting production-level models. You will work collaboratively across Data teams while ensuring that solutions are technically sound, reliable, and aligned with business needs.

• 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.

Requirements:

The ideal candidate brings solid professional experience in data science, strong Python and machine learning capabilities, and the ability to translate analytical work into practical outcomes. You should be comfortable working independently while collaborating closely with multidisciplinary teams and communicating technical concepts to diverse audiences.

• 3–5 years of professional experience in data science or a closely related environment.
• Experience building and deploying production-level machine learning models.
• Degree in Mathematics, Computer Science, Machine Learning, or a related field.
• Strong proficiency in Python, including NumPy, pandas, and scikit-learn.
• Basic knowledge of PyTorch or TensorFlow.
• Strong experience with exploratory data analysis and feature engineering.
• Solid understanding of statistics and probability, including hypothesis testing, inference, and distributions.
• Experience with supervised and unsupervised machine learning, including model tuning and validation.
• Strong understanding of model evaluation, cross-validation, performance metrics, and overfitting.
• Proficiency in SQL for data analysis and querying.
• Familiarity with ML experimentation tools such as MLflow, Weights & Biases, or Databricks ML.
• Basic familiarity with cloud ML platforms such as Azure ML, AWS SageMaker, or GCP Vertex AI.
• Experience with data visualization tools including Matplotlib, Seaborn, Plotly, Power BI, or Tableau.
• Understanding of fundamental MLOps concepts, including model registries, versioning, and deployment lifecycles.
• Strong analytical thinking and problem-solving skills.
• Ability to explain technical concepts and insights clearly to technical and non-technical audiences.
• Collaborative mindset and ability to work effectively across multiple Data teams.
• Structured, adaptable, and value-oriented approach to solving problems.
• Strong sense of ownership and attention to model quality and reliability.
• Proactive communication skills.
• English proficiency at a minimum B2 level.

Benefits:

• 100% remote work opportunities.
• Flexibility to work from the location where you are most comfortable and productive.
• International career opportunities and exposure to global projects.
• Collaboration with teams and projects across multiple international markets.
• Professional growth in a dynamic and collaborative technology environment.
• Opportunity to work on diverse Data, analytics, and machine learning projects.
• Health insurance.
• Life insurance.
• International and multicultural working environment.
• Support for eligible employees relocating from outside the European Union through the company's Tech Visa framework.