Details:

Desired Candidate Profile

1. Experience managing a team of Data Analysts/Scientists to deliver results and measure their success.
2. An established career in Analytics, OR a proven desire to build a career in Analytics – any academic course in data science, Kaggle record, certifications.
3. A strong understanding of Computer Vision / Natural Language Processing , Machine learning, Deep learning, Image analysis, statistics.
4. Experienced in programming in python with relevant open source software packages (e.g. tensorflow, keras, scipy, opencv, etc.).
5. Conceptual understanding of Statistical modeling.
6. Understanding of Machine Learning algorithms (Regression, SVM, Decision Trees, PCA, Clustering, etc..).
7. Experience in best practices in machine learning model development, software development, version control, and continuous integration and deployment.
8. Have worked on cross-disciplinary agile teams.
9. Excellent communication skills.

Min. Qualification:

1. Graduation /Postgraduate
2. Background in Mathematics or Computer Science is desirable

Skills Required:

Cloud, ML & AI, data science

Roles:

1. Leadership: Create and manage AI/ML product pipelines and put them into production. Together with Data scientists/engineers, design and develop production-ready AI/ML architectures using scalable, performant, reliable, and secure code.
2. Direct strategic technical vision for the company, anticipate, plan and act on shifts in technology in the fields of cloud servicing, AI, Machine learning, natural language processing, computational linguistics, and data science (not limited to data processing and encryption).
3. Attention to detail including precise and effective communications and proven ability to manage multiple, competing for priorities simultaneously.
4. Analytical Knack: Identify use cases and cast identified problems into well-defined components and requirements, and deliver end-to-end development of AI solutions.
5. Together with the Data science team, functional team, and leadership team, determine the feasibility of AI solutions to address customer and business needs.
6. Optimize models to balance trade-offs between accuracy and computational resource performance.
7. Implement integrated monitoring of model performance for reliability and maintenance.