- 6+ years of practical experience in data science, including deploying models to production.

- Expertise in Python and SQL;

- Solid background in ML frameworks such as scikit-learn, TensorFlow, PyTorch, and XGBoost.

- Competence in data visualization tools like Tableau, Power BI, matplotlib, and Plotly.

- Comprehensive knowledge of statistics, machine learning principles, and experimental design.

- Experience with cloud platforms (AWS, GCP, or Azure) and Git for version control.

- Exposure to MLOps tools and methodologies (e.g., MLflow, Kubeflow, Docker, CI/CD).

- Familiarity with NLP, time series forecasting, or recommendation systems is a plus.

- Knowledge of big data technologies (Spark, Hive, Presto) is desirable.

- Bachelors or Masters degree in Computer Science, Engineering, or a related field

Key Responsibilities :

- Lead and execute end-to-end data science projects, encompassing problem definition, data exploration, model creation, assessment, and deployment.

- Develop and deploy predictive models, optimization techniques, and statistical analyses to address tangible business needs.

- Articulate complex findings through clear and persuasive storytelling for both technical experts and non-technical stakeholders.

- Spearhead experimentation methodologies, such as A/B testing, to enhance product features and overall business outcomes.

- Partner with data engineering teams to establish dependable and scalable data infrastructure and production-ready models.

- Guide and mentor junior data scientists, while also fostering team best practices and contributing to research endeavors.