Data Scientist

Carry out end-to-end development and delivery of AI solutions including data pre-processing, data analysis, model building, fine tuning, deployment in cloud environments and maintenance.

Design, construct, install, test, and maintain data management systems to meet business requirements and industry practices.

Develop and implement scripts for database maintenance, deployment, and ETL operations.

Manage cloud services, particularly AWS and Azure, and other cloud storage systems relevant for the development of data and AI solutions.

Monitor data performance and modify infrastructure as needed to improve existing pipelines and models.

Collaborate with data architects, cloud architects, software developers and other IT specialists on project goals.

Assist in developing and documenting feature lists, user stories, and roadmaps aligned with the overarching product vision.

Collect and synthesize product feedback from stakeholders through data analysis, surveys, concept testing, analytics tools, and A/B testing.

Communicate complex concepts effectively to non-technical stakeholders.

Stay updated with the latest technology trends related to data science, data engineering and AI.

Be involved in execution of academic and research projects relevant from the overall Enterprise AI organization perspective.

Support ideation process during various internal and external ideation sessions with own technical and entrepreneurial approach and experience.

Proactively identify business opportunities through technology driven innovation.

Participate in knowledge sharing activities to elevate company on AI related topics.

Required skills
5-8 years of experience in end-to-end development and delivery of data and AI solutions.
Familiarity with Databricks and experience with repository management tools like Git.
Over 5 years of experience with cloud services: primarily AWS and / or Azure.
Strong knowledge of Python, SQL, and experience in Java or Scala.
Experience with big data technologies such as Spark.
Familiarity with supervised and unsupervised machine learning tools and methods as well as with mathematical programming techniques (e.g. mixed integer linear programming).