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Data Scientist
This job description is written in English, the working language for this role.
About the role
As a Data Scientist, you will be instrumental in developing end-to-end machine learning and statistical pipelines, from data exploration and feature engineering to model development, validation, and deployment. You will translate complex business requirements into measurable modeling objectives and success metrics, ensuring data quality, governance, and compliance throughout the modeling lifecycle. This role requires a proactive individual capable of working independently, taking ownership of projects, and clearly communicating findings and model results to diverse stakeholders.
What you’ll do
- Design, build, and deploy data models that turn company data assets into new products, services, and revenue streams (e.g. scoring models, data products, APIs, insights as a service).
- Identify and evaluate opportunities to create business value from data, in collaboration with product, business, and engineering teams.
- Develop end to end ML/statistical pipelines: data exploration, feature engineering, model development, validation, and deployment.
- Translate business requirements into measurable modeling objectives and success metrics (e.g. revenue impact, model accuracy, adoption).
- Monitor and maintain model performance in production; retrain or improve models as needed.
- Ensure data quality, governance, and compliance (privacy, security) throughout the modeling lifecycle.
- Communicate findings and model results clearly to both technical and non-technical stakeholders.
- Work independently with minimum supervision, able to plan, prioritize, and execute projects end to end.
- Mentor junior data scientists/analysts as needed (for senior level candidates).
What you’ll bring
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related field.
- Minimum 5 years of experience in data science/machine learning roles, with a track record of deploying models into production.
- Strong proficiency in Python and/or R, SQL, and ML frameworks (scikit-learn, XGBoost, TensorFlow/PyTorch, etc.).
- Solid understanding of statistical modeling, machine learning algorithms, and model evaluation techniques.
- Proven ability to work independently, take ownership of projects, and deliver results with minimum supervision.
- Strong business acumen, able to connect technical modeling work to business/revenue outcomes.
- Excellent communication and stakeholder management skills.
Skills
PythonRSQLscikit-learnXGBoostTensorFlowPyTorchStatistical modelingMachine learning algorithmsModel evaluation techniquesSparkHadoopAWSGCPAzureFraud modelingAnomaly detectionRisk scoringTransaction monitoring