Sr. AI Execution Specialist

2 days ago

Jordan Arab Bank Full time 30,000 - 60,000 per year

Qualifications:

Education:

· Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, or a related field.

Experience:

· At least from (5-7 )years of experience in data science or a related field, with a proven track record in delivering AI and advance data-driven solutions.

Competencies:

Technical:

· Proficiency in statistical analysis software, such as R.

· Expertise in data visualization tools like Tableau or Power BI.

· Advanced programming skills in Python and familiarity with machine learning frameworks such as scikit-learn or TensorFlow.

· Strong knowledge of relational databases (e.g., SQL, Oracle) and NoSQL databases (e.g., MongoDB).

· Experience with data pipeline tools and workflow analytics platforms like Alteryx or Dataiku.

Personal:

· Fluent in English and Arabic

· Exceptional communication, presentation, and interpersonal skills.

· Strong critical thinking and analytical problem-solving abilities

Roles & Responsibilities:

· Predictive Modeling and Machine Learning\: Develop and apply machine learning models to predict outcomes and optimize business decisions.

· Data Cleansing and Preprocessing\: Ensure high data quality and readiness for analysis through rigorous cleaning and preprocessing.

· Advanced Data Pipeline Development\: Design and implement sophisticated data pipelines to efficiently collect, store, and process data from diverse sources.

· Data Architecture Collaboration\: Work with data engineers to design scalable, reliable, and secure data architectures.

· Advanced Data Analytics\: Utilize complex statistical techniques and software tools like Python and R for in-depth data analysis.

· Pattern Recognition and Insight Generation\: Identify trends and patterns in data to derive actionable insights and drive business strategy.

· Data Visualization and Reporting\: Create compelling data visualizations and automated reports to effectively communicate insights to stakeholders.

· Collaboration Across Functions\: Engage with cross-functional teams, including marketing, sales, and product development, to integrate data-driven insights into business processes.

· Continuous Improvement and Innovation\: Enhance data science methodologies and remain updated with industry advancements and best practices.

· Mentorship and Training\: Mentor and train team members in advanced data science techniques and tools.

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