Senior Data Scientist - Machine Learning/Generative AI (3-7 yrs)

Job Overview :

We are seeking a highly skilled and experienced Senior Data Scientist who possesses a robust understanding of both traditional machine learning (ML) methodologies and innovative Generative AI techniques. This role is crucial for leveraging data to drive business decisions and enhance operational efficiencies. The ideal candidate will be adept at tackling complex data challenges and will have strong capabilities in model optimization, scalability, and MLOps practices. This expertise is essential to ensure seamless deployment, continuous monitoring, and effective management of machine learning models in production environments. By leading AI/ML initiatives, the Senior Data Scientist will play a key role in driving innovation and delivering significant value through the power of data and artificial intelligence.

Technical Skills :

- Machine Learning Expertise : The candidate should demonstrate proven experience in traditional machine learning techniques, including but not limited to regression, classification, decision trees, and ensemble methods. Additionally, a strong grasp of Generative AI models, such as GPT, BERT, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs), is essential.

- Model Optimization : A deep understanding of model optimization techniques is required, including quantization, pruning, and distillation. The candidate should be skilled in designing scalable architectures for AI/ML models, ensuring that they can handle varying data volumes and complexity while maintaining performance.

- Proficiency in MLOps : The role demands proficiency in MLOps tools and frameworks, such as MLflow, Kubeflow, Apache Airflow, and TensorFlow Extended. Familiarity with cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure for deploying machine learning models is critical for this position.

- Programming Skills : Strong programming skills in Python are essential, along with experience using machine learning and deep learning frameworks like TensorFlow, PyTorch, scikit-learn, and Hugging Face. The candidate should be comfortable writing efficient code and developing complex algorithms.

- Data Engineering Experience : Experience with data engineering pipelines and big data technologies is required. Familiarity with tools and frameworks such as Apache Spark and Hadoop, along with proficiency in databases (SQL and NoSQL), will be beneficial for the role.

Soft Skills :

- Problem-Solving Ability : The successful candidate must possess strong problem-solving skills and demonstrate the ability to work both independently and collaboratively within a fast-paced, dynamic environment. Adaptability and resourcefulness are key to navigating challenges effectively.

- Communication Skills : Excellent communication skills are crucial, particularly the ability to explain complex technical concepts clearly to both technical and non-technical stakeholders. The role will require conveying insights and recommendations based on data analysis in a manner that is accessible to diverse audiences.

- Leadership and Mentoring : Proven leadership experience is important, as the candidate will be responsible for mentoring junior team members and guiding them in their professional development. The ability to drive strategic AI/ML projects and motivate the team is essential for success.

Preferred Qualifications :

- Educational Background : A Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field is preferred, as it provides a strong foundation for the technical skills required in this role.

- Experience : Candidates should have a minimum of 3+ years of experience working on machine learning projects, particularly with traditional ML methods. Experience in deploying solutions in a cloud environment is highly advantageous.

- Business Acumen : Familiarity with business problem-solving techniques and the ability to create impactful insights through data analytics is essential. The candidate should be able to translate data findings into actionable business strategies.

- Cloud Platform Familiarity : Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for ML deployment is highly desirable, enabling the candidate to utilize cloud-native features and tools effectively.