Slav Petkovic

Responsibilities:

• Collaborate with external clients to understand business objectives and design custom algorithms aligned with their key performance indicators (KPIs).
• Support and maintain Azure-based data engineering pipelines utilizing Databricks and Snowflake.
• Lead the implementation of Scrum methodology across teams to enhance agile practices.
• Actively develop leadership capabilities guided by the “leaders worth following” philosophy, promoting growth and accountability.
• Continuously expand expertise in data engineering, machine learning, and AI development to deliver innovative, client-focused solutions.
• Practice servant leadership to foster healthy communication, and a positive team culture.
• Partner with the business development team by providing technical insight during meetings with prospective clients.
• Develop proof-of-concept AI solutions using Databricks Mosaic AI to demonstrate real-world applications and value to clients.

Responsibilities:

• Collaborate with leadership to identify and address business needs through data analytics.
• Developed a tailored MSSQL Data Warehouse, optimizing data modeling and reporting processes.
• Designed and implemented ETL processes for data integration from diverse sources, using custom Python API scripts and platforms like Funnel and Stitch.
• Rebuilt all company reports in Power BI to track key performance indicators (KPIs) such as Revenue, Margin, Orders, Online Sessions, and Customer Lifetime Value, with real-time insights.
• Lead analytics studies on large data sets using statistical techniques and CRISP-DM methodology, providing actionable insights related to inventory management and customer retention.
• Leverage Python for descriptive and predictive analytics, applying machine learning algorithms for time series analysis, customer segmentation, and sentiment analysis.
• Developed AI Agents based on Retrieval-Augmented Generation using Large Language Models (LLMs) based on Llamaindex to enhance internal analytics.
• Lead the Business Intelligence/Data Science team, driving key strategic initiatives and mentoring team members in Power BI, Python, Machine Learning, and AI.
• Led the cross-functional team during the successful migration of data from legacy systems to Shopify, designing and executing ETL processes for smooth data migration.
• Played a key role in post-migration QA, ensuring data accuracy, integrity, and functionality, reducing the margin of error to under 1%.
• Performed data engineering duties, utilizing BigQuery and Looker Studio to create and optimize data tables and views, supporting marketing reporting KPIs.

Responsibilities:

• Engineered ETL processes for client-specific projects using Python and frameworks like SQLAlchemy, Pandas, SocketIO, Flask, and Django, increasing data integration efficiency by 25%.
• Developed and maintained the company’s proprietary Python library for predictive analytics, improving forecasting accuracy by 15%.
• Created a real-time Occupancy Manager dashboard using Python and JavaScript/Bootstrap, enabling clients to monitor occupancy, enforce social distancing, and ensure mask presence during COVID.
• Developed an End-to-End data pipeline utilizing an on-prem SQL Server Database, Azure Data Lake, Azure Synapse, and Power BI, streamlining data processing and reporting workflows.
• Implemented machine learning algorithms for computer vision on Raspberry Pi and Jetson Nano, and Xovis platforms, achieving 100% automation and fully replacing manual tracking.
• Designed a customer behavior analysis system using ARIMA and FbProphet for time series analysis and predictive modeling of occupancy and transaction trends.
• Developed custom machine learning algorithms for facial recognition, mask detection, and customer distance tracking, increasing system accuracy by 10%.
• Trained associates in Excel, Python, Analytics and visualization.

Responsibilities:

• Created efficient Python ETLs optimizing data workflows and cutting processing time by 90%.
• Developed automated scrapers for product data enrichment, reducing manual data entry by 50%.
• Created a Python application for UPC Check Digit calculation based on the GS1 algorithm, ensuring accurate product identification.
• Conducted data mining and modeling using machine learning techniques, including clustering, KNN, time series, and regression analysis.
• Developed a Data Management Database that optimized daily analysis processes, leading to a 99% increase in data accessibility.
• Developed and maintained Tableau KPI reports for the Data team, improving metrics visibility.
• Implemented an image management process using Python, OpenCV, and Azure Blob Storage, reducing manual image classification process from 5 days to less than an hour.
• Developed a custom similarity algorithm for comparing product attributes increasing data matching accuracy by 75%.
• Mentored team members in Tableau, SQL and Excel, improving team productivity and data literacy.

Responsibilities:

• Conducted detailed data analysis using Excel and SQL to inform deployment requirements, ensuring system scalability and reliability.
• Provided technical leadership in project management, guiding architecture design and problem resolution for multiple high-impact projects, including collaborations with key partners.
• Led system configuration and validation initiatives, ensuring reliable data delivery, and reducing system errors by 20%.
• Served as the primary tester for proprietary hardware and user interfaces, ensuring successful deployments with minimal defects.
• Coordinated and trained new team members in India, fostering skill development and improving team productivity by 25%.
• Played a key role in deploying Real-Time Dashboards for major retailers, including Walmart, Giant Eagle, and Meijer, providing real-time insights that improved operational efficiency.
• Oversaw the deployment of Nomi technology within Walmart, ensuring seamless integration.

Responsibilities:

• Collected and compiled data from multiple sources (Access, Excel, SQL Server, Query Management Facility) for in-depth analysis and improving data accuracy.
• Conducted thorough data analysis that aligned with project goals and supported decision-making.
• Assessed operational challenges, investigated solutions, and prepared comprehensive KPI reports, driving performance improvements across teams.
• Collaborated with Labor, Repair, and Sales Execution teams to streamline cross functional processes.
• Analyzed and optimized field labor scheduling, improving operational efficiency and reducing labor costs in Tool Rental by 25%.
• Facilitated the deployment of repair software across multiple locations (Atlanta, Phoenix, Indianapolis), ensuring smooth transitions and operational continuity.
• Delivered training on new systems to field users, increasing operational proficiency and reducing onboarding time by 20%