Location
Noida | India
Job description
Qualification
- A Bachelor's degree in a related field (Computer Science, Statistics, Mathematics, Engineering, etc.). A Master's or Ph.D. is a plus.
- Minimum of 3.5 years of hands-on experience as a Data Scientist, with a strong focus on time-series data analysis, classification techniques, and experience in training machine learning models using a variety of techniques, including supervised, unsupervised, and reinforcement learning.
- Profound knowledge of supervised learning algorithms (e.g., regression, decision trees, support vector machines, neural networks) and unsupervised learning techniques (e.g., clustering, dimensionality reduction).
- Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) and experience in developing deep neural networks for classification tasks.
- Demonstrated ability to design, build, and optimize machine learning models for real-world applications.
- Ability to work with large and complex datasets.
- A systematic approach to experimentation, including A/B testing and cross-validation.
- Willingness to work in a fast-paced start-up environment.
- Effective communication skills to collaborate with cross-functional teams, present findings, and explain complex machine learning concepts to non-technical stakeholders.
Nice to have
- Working knowledge of pattern recognition and signal processing using Data analysis.
- Experience with cloud and software technologies
- Working experience with MLOps.
What will we do together As a Data Scientist in our team, we will collaborate to:
- Leverage Machine Learning Techniques: Work together to apply a wide range of machine learning methods, including supervised, unsupervised, and reinforcement learning, to solve complex problems and drive business outcomes.
- Model Development: Collaborate in the development of cutting-edge machine learning models, with a focus on classification tasks, from concept to deployment.
- Data Exploration and Preprocessing: Explore and preprocess data to create high-quality datasets, ensuring that our models are trained on the most relevant and reliable information.
- Evaluation and Optimization: Continuously evaluate model performance and optimize models for accuracy, efficiency, and scalability.
- Innovation: Encourage innovation and experimentation to push the boundaries of what machine learning can achieve within our organization.
- Cross-Functional Collaboration: Collaborate with diverse teams across the company, sharing insights and ensuring alignment with business objectives. 7. Professional Development: Support your ongoing professional development and growth in the field of machine learning and data science.
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