We are seeking a seasoned Senior Data Scientist with a passion for turning complex data into actionable insights and deploying machine learning models that drive business value. You will work on cutting-edge projects, collaborate with cross-functional teams, and play a pivotal role in shaping our data strategy and analytics capabilities.

Key Responsibilities :

- Design, develop, and deploy robust machine learning models and data-driven solutions to solve real-world business problems.

- Analyze large, complex datasets to extract meaningful patterns and insights that inform product and business decisions.

- Collaborate closely with product managers, engineers, and stakeholders to define data requirements and deliver analytics solutions.

- Develop, test, and maintain scalable ML pipelines and automate model deployment using MLOps best practices.

- Lead end-to-end model lifecycle management including data preprocessing, feature engineering, model training, evaluation, and monitoring.

- Create intuitive and impactful data visualizations and dashboards to communicate findings to both technical and non-technical audiences.

- Mentor junior data scientists and analysts, fostering a culture of continuous learning and innovation.

- Conduct rigorous statistical analyses and experimental designs to validate hypotheses and optimize product features.

- Stay current with the latest advancements in data science, machine learning, and AI, recommending and implementing innovative techniques.

- Ensure data integrity, quality, and security throughout the analytical process.

Required Qualifications & Skills

- Masters or PhD in Computer Science, Statistics, Mathematics, or a related discipline.

- 5+ years of hands-on experience in data science, including deploying machine learning models into production environments.

- Strong programming skills in Python and proficiency in SQL for data extraction and manipulation.

- Deep expertise in ML frameworks such as scikit-learn, TensorFlow, PyTorch, and XGBoost.

- Experience with data visualization tools like Tableau, Power BI, matplotlib, and Plotly to create compelling dashboards and reports.

- Solid understanding of statistics, machine learning principles, experimental design, and hypothesis testing.

- Proven experience with cloud platforms such as AWS, GCP, or Azure, including managing data and compute resources.

- Familiarity with Git and version control workflows in a collaborative environment.

- Practical knowledge of MLOps tools and methodologies such as MLflow, Kubeflow, Docker, and CI/CD pipelines.

- Experience or interest in NLP, time series forecasting, or recommendation systems is a strong plus.

- Understanding of big data technologies like Spark, Hive, and Presto is desirable.