onsite
Data Scientist
Data Scientist
Contentful is seeking a Data Scientist to transform data into business insights and build scalable models. This role involves developing predictive models, statistical analyses, and machine learning algorithms, as well as collaborating with cross-functional teams to deploy data products.
About the role
About the opportunity
We are looking for a Data Scientist who will turn data into insights and build models that drive meaningful business outcomes. You will work closely with cross-functional stakeholders to understand problems, design analytical solutions, and deploy data products that scale.
What to expect?
- Collect, clean, and analyze structured and unstructured data.
- Develop predictive models, statistical analyses, and machine learning algorithms.
- Build dashboards, reports, and data visualizations to communicate insights.
- Collaborate with engineering to deploy models into production environments.
- Perform exploratory data analysis (EDA) to identify trends and opportunities.
- Conduct A/B testing and design experiments to measure feature performance.
- Document methodologies and ensure reproducibility of analytical work.
- Contribute to end-to-end machine learning workflows, including model training, validation, and deployment, working with established tools and patterns and with support from engineering.
- Monitor model performance and data quality, and update models as needed.
What do you need to be successful?
Required
- Bachelor’s or Master’s in Data Science, Computer Science, Statistics, Mathematics, or related field.
- Strong proficiency in Python (pandas, scikit-learn, NumPy).
- Experience using SQL to work with analytical databases and data warehouses.
- Solid understanding of statistical modeling, model evaluation, and experimental methods.
- Familiarity with machine learning techniques (regression, classification, clustering, etc.).
- Hands-on experience building and running machine learning models in production or production-like environments.
- Experience building dashboards with tools like Tableau, or similar.
- Strong analytical and problem-solving skills.
- Ability to balance analytical rigor with practical delivery.
- Excellent communication skills with the ability to translate data insights into business actions.
Preferred
- Familiarity with workflow orchestration or model lifecycle tools (e.g., Airflow, MLflow) at a practical, working level.
- Implement practical model deployment approaches (e.g., batch inference, scheduled retraining) using existing team tools and patterns.
- Experience with cloud platforms (AWS, or similar).
- Knowledge of ML Ops tools (MLflow, Airflow, or similar).
- Exposure to big data technologies (Redshift, Snowflake).
- Experience working in agile environments.