
Data Scientist
2 days ago
About The Role
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.
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