Data Scientist - SQL/Python

About The Role

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.