About the Role
The Data Scientist will be involved in the delivery of end-to-end analytics projects, working closely with both technical teams and business stakeholders. The role requires a strong analytical mindset combined with the ability to understand business contexts and translate data insights into actionable recommendations.
The position will be responsible for exploring, cleaning, and analyzing large and complex datasets from multiple sources, ensuring data quality and consistency throughout the project lifecycle. The Data Scientist will design, develop, and validate statistical models and machine learning algorithms to support use cases such as forecasting, customer segmentation, pricing optimization, churn prediction, and risk analysis.
A key responsibility of the role is collaborating with client stakeholders to understand business objectives, define analytical requirements, and frame problems in a data-driven way. This includes participating in workshops, presenting findings, and clearly communicating insights to non-technical audiences through data visualizations and structured storytelling.
The Data Scientist will contribute to the deployment and operationalization of analytical models, working alongside data engineers and platform teams to ensure scalability, performance, and reliability. The role will also support the continuous improvement of analytical methodologies, coding standards, and best practices within the analytics team.
The position requires working in a consulting environment, managing multiple projects in parallel, and adapting to different client contexts. The Data Scientist will report to an Analytics Manager and collaborate with multidisciplinary teams across different geographies.
Requirements
3 to 6 years of professional experience as a Data Scientist, Data Analyst, or in a similar analytics-focused role, preferably within consulting firms, data-driven organizations, or complex business environments.
Strong programming skills in Python, with hands-on experience in data manipulation, analysis, and modeling using libraries such as pandas, NumPy, scikit-learn, and similar frameworks. Solid ability to write clean, efficient, and well-documented code.
Proficiency in SQL for querying large and complex datasets, with experience working across different database systems and data warehouses.
Strong background in statistics and machine learning, including experience with supervised and unsupervised learning techniques, model validation, feature engineering, and performance evaluation.
Experience working with end-to-end analytics workflows, from data exploration and feature selection to model development, testing, and deployment in production or near-production environments.
Ability to translate analytical results into clear business insights, demonstrating a strong understanding of business drivers and the ability to formulate data-driven recommendations that support decision-making.
Experience communicating with non-technical stakeholders, including presenting results through data visualizations, dashboards, and structured presentations tailored to executive and business audiences.
Familiarity with data visualization tools (e.g. Tableau, Power BI, or similar) and with version control systems such as Git is considered a strong plus.
Strong problem-solving skills, intellectual curiosity, and the ability to work independently while contributing effectively to cross-functional and project-based teams.
Fluency in English is required for working in an international consulting environment; knowledge of French is considered an advantage.
About the Company
Our client is a European data and advanced analytics consultancy supporting medium and large organizations across sectors such as financial services, retail, and telecommunications. The company partners with its clients to address complex business challenges by leveraging data, advanced analytics, and artificial intelligence.
Operating at the intersection of technology and business strategy, the organization delivers end-to-end analytics solutions, from data strategy and architecture to model development and operationalization. The company is recognized for its strong analytical expertise, pragmatic approach, and ability to translate complex data into measurable business impact.
As demand for data-driven decision-making continues to grow, the organization is expanding its analytics teams to support an increasing number of large-scale, high-impact projects. The working environment is collaborative, international, and focused on continuous learning and technical excellence.