Description :

Role Summary :

We are seeking a highly skilled Data Scientist to design and implement advanced analytics, machine learning models, and data-driven insights that support business decision-making.

The ideal candidate will have strong analytical abilities, hands-on experience with machine learning techniques, and the ability to translate complex data into actionable recommendations across business functions.

Key Responsibilities :

Data Analysis & Modeling :

- Collect, clean, and analyze large datasets from various sources.

- Build, train, and optimize machine learning and statistical models to solve business problems.

- Apply predictive analytics, clustering, classification, regression, and NLP techniques as needed.

Data Engineering & Preparation :

- Develop data pipelines and prepare datasets for analysis and modeling.

- Work with data engineers to ensure high-quality, scalable data flows.

- Implement feature engineering, data transformation, and dimensionality reduction.

Business Insights & Visualization :

- Translate technical findings into meaningful business insights and recommendations.

- Create dashboards and visualizations using tools like Tableau, Power BI, or Python libraries.

- Present insights to stakeholders, leadership, and cross-functional teams.

Model Deployment & Monitoring :

- Deploy machine learning models into production environments.

- Collaborate with MLOps and engineering teams to integrate models into business systems.

- Monitor model performance, drift, and accuracy over time, and implement retraining strategies.

Cross-functional Collaboration :

- Work closely with product managers, domain experts, and engineering teams.

- Understand business requirements and design analytical solutions accordingly.

- Support experimentation, A/B testing, and data-driven decision initiatives.

Required Skills & Experience :

- Bachelors or Masters in Computer Science, Data Science, Statistics, Mathematics, or related field.

- Strong experience in Python or R for data analysis and machine learning.

- Proficiency with machine learning libraries : scikit-learn, TensorFlow, PyTorch, XGBoost, etc.

- Hands-on experience with SQL and working with large datasets.

- Experience with data visualization tools (Tableau, Power BI, matplotlib, seaborn).

- Strong understanding of statistics, probability, and experimental design.

- Experience with cloud platforms like AWS, Azure, or GCP (SageMaker, Databricks, etc.

- Familiarity with version control (Git) and MLOps tools