Job Summary
This is a challenging role that requires expertise in developing prototypes and data-centric solutions for various users using Data Analytics (DA) and Artificial Intelligence (AI).
The ideal candidate will provide guidance, support, and training for AI and DA tasks for both technical and non-technical audiences.
Key responsibilities include preparing architectural outputs related to AI and DA, collecting and analyzing business requirements, performing technical analyses, creating specifications for AI and DA solutions, conducting exploratory data analyses to identify, interpret, and analyze patterns and trends in complex datasets for diagnosis and prediction, designing, modeling, developing, managing, and reviewing end-to-end DA and AI products, prototypes, and proof of concepts on both on-premise and cloud data platforms, implementing MLOps practices, including automated testing and quality assurance, defining and documenting training and support procedures, maintaining DA and AI products, architecting, designing, deploying, and managing components of data platforms, integrating and reusing COTS, SaaS, PaaS, and IaaS solutions, designing and implementing data protection and security measures, and defining testing strategies, designing tests, and conducting testing.
Requirements
* Bachelor's degree in Computer Science, Information Technology, or any related field.
* Business analysis skills.
* Strong analytical skills, capable of identifying patterns and gaps while exploring data.
* Deep knowledge of statistical analysis and data-driven decision-making.
* Extensive experience with Data Science and AI techniques, particularly in prediction, machine learning, and Generative AI.
* Proficient in programming languages for data analytics and AI.
* Experienced in machine learning algorithms and deep learning frameworks.
* Skilled in developing reusable AI models and Generative AI APIs.
* Experienced in the industrialization of DA and AI solutions.
* Knowledgeable about MLOps tools, testing, and quality assurance.
* Proficient with visualization tools.
* Knowledgeable in SQL and No-SQL databases.
* Experienced with orchestration tools and ETL tools.
* Familiar with search engines.
* Knowledgeable in data modeling for data lakes and data warehouses.
* Experienced with cloud environments and containerization.
* English fluency.