Mohammed Zakyi

Data Scientist with experience delivering predictive analytics, business intelligence, and applied AI solutions across enterprise environments. Strong background in Python, SQL, Power BI, machine learning, and statistical analysis, with a track record of improving decision-making, operational efficiency, and revenue performance through data-driven insights and automation. Built practical AI applications including a voice assistant, private-data chatbot, AI-powered SSIS documentation tool, and sales forecasting solutions that turn analytics into repeatable, scalable business workflows.

Data Scientist - AT&T Mobility Solutions LLC

(2022-07)

• Support analytics and decision-making across AT&T product and sales environments by transforming complex data into predictive insights, reporting tools, and business recommendations.
• Apply machine learning and analytics to sales portal optimization initiatives, improving client traffic by 20% and generating approximately $400K in additional monthly revenue.
• Build and refine predictive models using feature engineering and hyperparameter tuning to improve forecasting accuracy and business applicability across sales and customer-focused initiatives.
• Develop Power BI dashboards and reporting solutions that provide real-time visibility into KPIs, model outputs, and growth opportunities for business stakeholders.
• Conduct statistical analysis and trend modeling to uncover customer behavior patterns and operational drivers, helping improve operational efficiency by 15%.
• Lead a Generative AI initiative that enhances team productivity by optimizing business processes, generating insights, and enabling more efficient data analysis workflows.
• Guide team members on GenAI best practices and mentor junior colleagues through major projects and data-driven storytelling.

Associate Data Scientist - Lifeline Tech

(2021-05 - 2022-07)

• Conducted exploratory data analysis and statistical modeling to identify trends, performance drivers, and business opportunities across operational and customer-related datasets.
• Developed and maintained Python- and SQL-based preprocessing pipelines to clean, transform, and validate large-scale data, reducing processing time from 5 days to 1 day while improving data quality by 10%.
• Built predictive models and automated analytical workflows to support forecasting, optimization, and more timely business reporting.
• Created interactive Power BI dashboards and visualizations that improved access to KPIs, model outputs, and analytical insights for business stakeholders.
• Partnered with data engineering teams on feature pipeline design and data structuring to improve readiness for machine learning and advanced analytics use cases.

Master of Science - Data Science - University of the Cumberlands (2023)

Bachelor of Arts - Economics - University of Michigan, Ann Arbor (2021)