Senior Data Scientist
hace 2 semanas
Tiger Analytics is the largest AI and advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring depth in the industry and deep expertise in Data Science, Data Engineering, Machine Learning, and AI. Various market research firms, including Forrester and Gartner, have recognized our business value and leadership. We are headquartered in Silicon Valley and have our global delivery center in Chennai, India. We also have a presence in Europe, Singapore and LATAM markets.
**Key Responsibilities**:
- Develop and deliver Advanced Analytics/Data Science solutions, focusing on DevOps/MLOps and Machine Learning models.
- Collaborate with data engineers and ML engineers to process and analyze data, optimizing analytics capabilities.
- Ensure timely and cost-effective project execution while adhering to enterprise architecture standards.
- Build and optimize data pipelines using big data technologies, including batch and real-time processing.
- Automate ML models deployment and the end-to-end ML lifecycle using Azure Machine Learning and Azure Pipelines.
- Monitor ML infrastructure by setting up cloud alerts, dashboards, and logging mechanisms.
- Troubleshoot and optimize machine learning infrastructure for scalability and efficiency.
- 8+ years of experience in Data Science and Machine Learning.
- 5+ years of hands-on experience in Python and PySpark.
- 4+ years of experience in Machine Learning (ML), with cloud service expertise (Azure preferred, AWS/GCP is a plus).
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field.
- Strong stakeholder management skills, including engagement with business units and vendors.
- Data Science: Strong expertise in developing supervised and unsupervised ML models, with knowledge of time series and demand forecasting being a plus.
- Programming: Hands-on experience with Python, PySpark, and SQL for data querying and statistical modeling.
- Statistics: Solid understanding of statistical tests, distributions, regression models, and maximum likelihood estimators.
- Cloud & Big Data: Experience in Databricks, Azure Data Factory (ADF), and familiarity with Spark, Hive, and Pig is advantageous.
- ML Deployment: Experience in deploying ML models, working with version control tools like GitHub, and implementing CI/CD pipelines.
- MLOps & Automation: Understanding of MLFlow, Kubeflow, and ML Ops automation frameworks.
- Problem-Solving: Strong analytical skills to handle complex data science challenges in commercial, net revenue management, or supply chain domains.
- Bias for action, with the ability to deliver outstanding results through task prioritization and time management.
- Be proactive, curious, can-do attitude, flexible personality
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
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