Senior Data Scientist

Knowledge / Experience / Competencies Required

IT Skills Experience (Priority wise):

1. Hands-on programming and architecture capabilities in Python.

2. Demonstrated technical expertise around architecting solutions around AI, ML, deep learning and Generative AI related technologies.

3. Experience in implementing and deploying Machine Learning solutions (using various models, such as GPT-4, Lama2, Mistral ai, text embedding ada, Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Topic Modeling, Game Theory etc. )

4. Understanding of Nvidia Enterprise NEMO Suite.

5. Expertise in popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras, for building, training, and deploying neural network models.

6. Experience in AI solution development with external SaaS products like Azure OCR

7. Experience in the AI/ML components like Azure ML studio, Jupyter Hub, TensorFlow Sci-Kit Learn

8. Hands-on knowledge of API frameworks.

9. Familiarity with the transformer architecture and its applications in natural language processing (NLP), such as machine translation, text summarization, and question-answering systems.

10. Expertise in designing and implementing CNNs for computer vision tasks, such as image classification, object detection, and semantic segmentation.

11. Hands on experience in RDBMS, NoSQL, big data stores like: Elastic, Cassandra.

12. Experience with open source software

13. Experience using the cognitive APIs machine learning studios on cloud.

14. Hands-on knowledge of image processing with deep learning ( CNN,RNN,LSTM,GAN)

15. Familiarity with GPU computing and tools like CUDA and cu DNN to accelerate deep learning computations and reduce training times.

16. Understanding of complete AI/ML project life cycle

17. Understanding of data structures, data modelling and software architecture

18. Good understanding of containerization and experience working with Docker, AKS.

People Skills:

Clear and concise communication is vital for explaining complex machine learning concepts to non-technical stakeholders, presenting results, and collaborating with cross-functional teams.

Ability to work independently and as part of a team.

Being open to new ideas, embracing change, and adapting to evolving technologies and methodologies are crucial for staying relevant and effective in the rapidly changing field of machine learning.

Cooperative mindset, flexibility, and the ability to work effectively in a team.

Professional and open communication to all internal and external interfaces.

Balancing multiple projects, prioritizing tasks, and meeting deadlines while maintaining a high standard of work requires effective time management and organizational skills.

Accurately report to management in a timely and effective manner.

Other Skills:

Outstanding analytical and problem-solving skills