Data Science Intern

Data ScienceWe are hiring on behalf of one of our clients for a Data Science opportunity in France. This role is suited to individuals who enjoy working with data, developing analytical models, and using statistical and machine learning techniques to solve practical business problems.Position: Data Science Intern

Location: France | RemoteOverviewAs a Data Scientist, you will work with structured and unstructured datasets to uncover patterns, build predictive models, and generate insights that support business and operational decisions.

You will contribute to the development, evaluation, and improvement of data-driven solutions.Key Areas of WorkData exploration and preparationStatistical and predictive analysisMachine learning model developmentModel evaluation and improvementData-driven problem solvingKey ResponsibilitiesCollect and prepare datasets from databases, APIs, files, and other relevant sources.Explore datasets to understand distributions, relationships, trends, and potential data quality issues.Perform feature engineering and transform variables to support machine learning models.Apply statistical techniques to investigate business questions and identify meaningful relationships.Develop and test supervised and unsupervised machine learning models for relevant use cases.Evaluate model performance using appropriate metrics and validation techniques.Compare different modelling approaches and refine models based on analytical results.Use Python and libraries such as Pandas, NumPy, Scikit-learn, or similar tools for data science tasks.Analyze model outputs and translate technical findings into practical insights.Support predictive analysis involving areas such as customer behaviour, demand, sales, operations, or other business use cases.Visualize datasets and model results using suitable charts, graphs, and analytical tools.Investigate model errors, unusual predictions, and unexpected patterns in analytical results.Document data preparation steps, modelling approaches, assumptions, and evaluation results.Work with data teams to improve datasets, modelling workflows, and analytical processes.Assist with deploying or integrating analytical models into practical data workflows where required.Stay informed about relevant machine learning techniques and identify opportunities to improve existing approaches.Required QualificationsStrong foundation in data science, statistics, and machine learning concepts.Proficiency in Python and experience working with data analysis libraries.Understanding of supervised and unsupervised learning techniques.Experience with Pandas, NumPy, Scikit-learn, or comparable tools.Knowledge of data cleaning, feature engineering, and exploratory data analysis.Understanding of model training, testing, validation, and performance evaluation.Familiarity with SQL and relational databases.Strong analytical and problem-solving skills.Ability to interpret model results and communicate findings clearly.Good attention to detail when preparing datasets and evaluating models.Preferred SkillsExperience with regression, classification, clustering, or time-series modelling.Knowledge of statistical testing and probability concepts.Familiarity with TensorFlow, PyTorch, or other machine learning frameworks.Exposure to natural language processing or other applied machine learning areas.Experience with data visualization tools such as Matplotlib, Seaborn, Power BI, or Tableau.Understanding of cloud-based data and machine learning platforms.Familiarity with Git and collaborative development workflows.Exposure to MLOps concepts, model deployment, or machine learning pipelines.Understanding of responsible AI, data privacy, and model governance principles.Professional working proficiency in French and English is an advantage.What You Can ExpectRemote opportunity supporting data science initiatives in France.Practical exposure to real-world datasets and machine learning workflows.Opportunity to work with Python, SQL, statistical methods, and machine learning tools.Experience developing and evaluating predictive and analytical models.Exposure to different business-focused data science use cases.Opportunity to strengthen skills in data preparation, modelling, and model evaluation.Practical projects that can contribute to a professional Data Science portfolio.