Senior Data Scientist - Machine Learning

Description :

- Design, develop, and deploy advanced machine learning and statistical models to solve complex business problems.

- Build scalable data pipelines and production-ready ML systems that can handle large-scale data processing.

- Implement and optimize algorithms for predictive modeling, classification, clustering, and recommendation systems.

- Conduct rigorous model validation, testing, and performance monitoring to ensure reliability and accuracy.

Technical Excellence :

- Develop and maintain clean, efficient, and well-documented code following best practices.

- Optimize model performance through feature engineering, hyperparameter tuning, and algorithm selection.

- Implement MLOps practices including model versioning, monitoring, and continuous improvement.

- Stay current with the latest developments in data science, machine learning, and AI technologies.

Cross-functional Collaboration :

- Partner with product managers, engineers, and business stakeholders to identify opportunities for data-driven solutions.

- Collaborate with data engineering teams to ensure data quality, accessibility, and infrastructure requirements are met.

- Work with software engineers to integrate models into production systems and applications.

- Present findings and recommendations to senior leadership and key stakeholders.

Required Qualifications :

Experience :

- 7+ years of professional experience in data science, machine learning, or related analytical roles.

- Proven track record of successfully delivering end-to-end data science projects from conception to production.

- Strong portfolio demonstrating impact through deployed models and data-driven solutions.

- Experience working with large-scale datasets and distributed computing environments.

Technical Skills :

- Expert proficiency in Python or R for data analysis and machine learning.

- Strong knowledge of machine learning frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.

- Advanced SQL skills and experience with various database systems (PostgreSQL, MySQL, NoSQL).

- Experience with big data technologies (Spark, Hadoop, Hive) and cloud platforms (AWS, GCP, or Azure).

- Proficiency in data visualization tools (Matplotlib, Seaborn, Plotly, Tableau, or Power BI).

Domain Knowledge :

- Deep understanding of machine learning algorithms including supervised and unsupervised learning techniques.

- Expertise in feature engineering, model selection, and hyperparameter optimization.

- Knowledge of deep learning architectures and applications (CNNs, RNNs, Transformers).

- Experience with natural language processing, computer vision, or time series analysis (depending on role focus).

- Understanding of model deployment, monitoring, and MLOps best practices.

Preferred Qualifications :

- Experience with real-time data processing and streaming analytics.

- Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.

- Experience with AutoML platforms and model optimization techniques.