Ashish Khachane

Conceptualized and implemented an SPP, an AI model that helps predict the sales target through historical data analysis and machine learning. I engineered a recommendation engine by implementing various business logic to enhance sales performance driven by the SPP model predictions and provided strategic guidance on data collection, storage, compliance, and quality assurance. Also, I contributed to end-to-end development of data frameworks, analysis, provisioning, modeling, and optimization. My other responsibilities included:

• Installing, managing, and optimizing scalable data pipelines for real-time and batch processing using Python, Amazon SageMaker, Snowflake, and Airflow.
• Demonstrating proficiency in utilizing version control and CI/CD systems to maintain efficient development workflows.
• Constructing large-scale, high-performance data structures for analytics purposes.
• Collaborating effectively with both technical and business stakeholders to drive data-driven decisions.
• Managing data collection, cleaning, utilization, and storage processes.
• Communicating data insights and coherently articulating their business impact through various storytelling formats such as slides, charts, and dashboards.

Deployed the EAMS project, reducing production downtime due to material shortages by 20%. I established a system to notify material planners of a potential material shortage based on inventory levels and customer demand forecasts, streamlining inventory management processes, and improving overall efficiency. My other duties included:

• Building data pipelines using PySpark SQL to automate inventory monitoring and alert generation by comparing material inventory against business-set thresholds.
• Integrating customer demand forecasting to ensure accurate material requirements.
• Leveraging Palantir Foundry tools, including Contour and Code Workbook, to analyze and tailor data, aligning insights with business requirements and increasing operational efficiency by 20%.
• Carrying out data health checks, data expectations, unit testing, code optimization, and tuning strategies to enhance performance and minimize resource consumption, fortifying data pipelines and ensuring their stability.
• Developing and maintaining schedules for executing data pipelines in the data lineage framework.

Analyzed the effectiveness of enablement programs aligned with business outcomes. I also quantified the impact of various treatments—like plays, certifications, and planning processes—on outcomes such as annual contract value (ACV), pipeline generation, and customer renewal. Additionally, I:

• Provided insights into the impact of events like training sessions and marketing campaigns.
• Pioneered APM as a comprehensive tool correlating employee attributes with treatment outcomes.
• Crafted an accountability performance matrix (APM), a dashboard driven by a data-model engine.
• Facilitated informed decision-making by comparing and analyzing data.

I designed and implemented a scalable A/B testing framework to assess the effectiveness of business interventions by comparing treated and control groups across various business units. I also conducted statistical analyses and hypothesis testing to confirm the significance of observed differences between treated and control groups, ensuring reliable and data-driven insights. Additionally, I quantified the impact of interventions, identifying a measurable improvement in outcomes (e.g., a 10% increase in sales) and isolating key drivers for success.