What you will do
- Collaborate with business stakeholders to understand requirements, identify business problems, formulate data science hypotheses, and define measurable success criteria.
- Design, develop, and implement advanced data mining, machine learning, and statistical solutions to address complex business challenges.
- Collect, extract, clean, and prepare structured and unstructured datasets for analysis and model development.
- Perform feature engineering and ensure data readiness for building reliable, production-grade machine learning models.
- Develop data models based on specific business requirements and analytical problem statements.
- Build, train, evaluate, and optimize predictive models using appropriate machine learning algorithms and statistical techniques.
- Apply model selection, hyperparameter tuning, cross-validation, and statistical significance testing to improve model performance.
- Develop machine learning and Natural Language Processing (NLP) solutions for applications such as forecasting, recommendation systems, anomaly detection, and sentiment analysis.
- Collaborate with Data Analysts, Data Engineers, and Data Architects to design scalable analytics architectures and reliable data pipelines.
- Work with UX and product teams to ensure effective presentation and interpretation of model outputs and analytical insights.
- Develop visualizations and dashboards to communicate model behaviour, performance metrics, and business insights.
- Design and conduct experiments, including A/B testing, to validate hypotheses and measure model effectiveness.
- Implement processes to monitor data accuracy, model performance, data drift, and potential bias.
- Deploy, maintain, and monitor machine learning models in production using MLOps practices.
- Build and manage CI/CD pipelines, model registries, automated testing, and reproducible model deployment processes.
- Ensure machine learning solutions comply with applicable regulatory frameworks, including GDPR and the EU AI Act.
- Implement AI governance practices covering model transparency, explainability, fairness, accountability, and risk management.
- Prepare technical documentation, model cards, validation reports, and model lifecycle documentation.
- Communicate model insights, limitations, and recommendations effectively to technical and non-technical stakeholders.
What you bring
- Strong experience in Data Science, Advanced Analytics, Machine Learning, and statistical modelling.
- Hands-on programming experience with Python, R, and SQL; knowledge of Perl is an added advantage.
- Experience with advanced analytics tools and frameworks such as Python, R, SAS, and Apache Spark.
- Strong knowledge of machine learning libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, and Hugging Face.
- Experience in developing predictive models, statistical models, and NLP-based solutions.
- Knowledge of MLOps practices, including CI/CD pipelines, model registries, automated testing, and model reproducibility.
- Experience with data visualization and business intelligence tools such as Tableau, SAS Analytics, and SAP Analytics.
- Understanding of data engineering and ETL processes using Talend, Informatica, dbt, or Azure Data Factory.
- Experience with SQL and NoSQL databases, including MongoDB, and large-scale data processing technologies such as Hadoop.
- Knowledge of scalable data storage architectures, including Data Lakes, Lakehouses, and distributed data storage systems.
- Experience in advanced analytics applications such as forecasting, recommendation engines, anomaly detection, and sentiment analysis.
- Understanding of AI governance principles, including transparency, explainability, fairness, and accountability.
- Knowledge of AI compliance, model risk management, bias monitoring, and model drift detection.
- Understanding of GDPR, EU AI Act, and responsible AI practices.
- Experience with experimental design techniques, including A/B testing, cross-validation, and statistical significance testing.
How you work
- Translate complex business problems into data science hypotheses and effective machine learning solutions.
- Identify suitable algorithms, statistical methods, and analytical approaches for different business challenges.
- Design, develop, test, and optimize machine learning models for accuracy, scalability, and performance.
- Interpret analytical results and translate model outputs into meaningful business recommendations.
- Present complex technical concepts, model behaviour, and insights clearly to technical and non-technical stakeholders.
- Ensure accuracy, reproducibility, transparency, and reliability throughout the model development lifecycle.
- Design and execute experiments to validate hypotheses and evaluate model performance.
- Identify and address model-related risks, including bias, data drift, and performance degradation.
- Collaborate effectively with Data Analysts, Data Engineers, Architects, UX teams, and business stakeholders.
- Manage multiple experiments, data pipelines, and project deliverables within agreed timelines.
- Adapt to emerging machine learning technologies, AI frameworks, algorithms, and analytical methodologies.
- Participate effectively in multilingual meetings and communicate fluently in English; knowledge of French is an added advantage.
- Demonstrate excellent interpersonal, communication, analytical, and problem-solving skills.
- Work independently as well as collaboratively within cross-functional teams.
- Maintain a proactive, flexible, and results-oriented approach.
What you can expect
- Opportunities to work on advanced Data Science, Machine Learning, and Artificial Intelligence initiatives.
- Exposure to modern AI/ML frameworks, cloud technologies, and scalable analytics platforms.
- Opportunities to develop and deploy production-grade machine learning solutions.
- Experience working with MLOps, responsible AI, and emerging AI regulatory frameworks.
- Collaboration with multidisciplinary teams across Data Science, Data Engineering, Architecture, and Business.