
Principal Analytics Lead
6 days ago
We are seeking a highly skilled Senior Data Scientist to drive business decision making by extracting actionable insights from structured and unstructured data.
The ideal candidate will have 3-5 years of experience in data analytics and machine learning, with hands-on experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
The successful candidate will be proficient in writing production-quality code in Python, with a strong understanding of object-oriented programming principles. Experience in analyzing and working with large datasets, as well as expertise in applying both supervised and unsupervised learning techniques, is also required.
Apart from technical skills, the ideal candidate should possess strong collaboration, communication, and creative thinking skills. They should be open to new ways of thinking and committed to acquiring new skills to retain a competitive advantage.
The role involves conducting end-to-end machine learning and data analytics activities, including data wrangling, feature engineering, building and evaluating ML models. It also requires researching and implementing novel machine learning approaches to enable informed business decisions.
In addition to these responsibilities, the successful candidate will perform data engineering, feature engineering, and data preprocessing to train and tune ML models. They will also utilize Business Intelligence tools to prepare reports, analyze and visualize data.
Last but not least, the candidate will implement MLOps practices such as Continuous Integration/Continuous Deployment (CI/CD) and continuous training (CT) for deploying and maintaining ML models in production environments.
Your Key Responsibilities:
- Drive data-driven business decisions using machine learning and data analytics
- Develop and maintain high-quality Python code with a strong understanding of object-oriented programming principles
- Collaborate with cross-functional teams to deliver business results
- Stay up-to-date with industry trends and emerging technologies
- Implement best practices in data engineering, feature engineering, and data preprocessing
- Utilize Business Intelligence tools to prepare reports, analyze and visualize data
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