Athulya Surendran Krishnaleela

About Athulya Surendran Krishnaleela

Senior Product & Experimentation Data Scientist with 6+ years of experience in data, along with Master\'s, supporting data modeling and warehousing, BI, product analytics, feature deployment, and experimentation across consulting, retail last-mile (capacity planning and delivery experience), energy, and healthcare Key Competencies- Strategic Vision: Proven ability to develop and implement strategies for business growth and operations based on customer behavior - Data-Driven Optimization: adept at leveraging data and analytics to streamline operations, reduce costs, and enhance decision-making- Project Management Excellence: Track record of successfully managing end-to-end project lifecycles with a focus on delivering high-quality, on-time outcomes- Technical Proficiency: Skilled in a wide array of tools and technologies, from SQL and Python to advanced machine learning libraries and cloud platforms. Skills & Tools:Analytics & Experimentation: A/B Testing, Experiment Design, Causal Analysis, KPI Design, Product Analytics, Behavioral Analytics, Survey Design, Metric AnalyticsData & Engineering: Advanced SQL, Python, ETL/ELT Pipelines, Data Warehousing, Redshift, BigQuery, Snowflake, DBT, Airflow, VS Code, Docker, GitVisualization & BI: QuickSight, Tableau, Power BI, LookerCloud & Platforms: AWS (S3, Redshift, Lambda), GCP (BigQuery), Azure DatabricksE-mail: s••••••••@gmail.com

Experience

1. Business Intelligence Engineer Last Mile (Capacity Planning & Delivery Experience)

   Amazon

   Feb 2024 — Present · Bengaluru, IN

   Capacity/Labor Planning: Led the launch of ALPS (Automated Labor Planning System) across 53 JP last-mile delivery sites, automating labor planning workflows and dashboards. Extended the EU architecture to JP to ensure consistent metrics and pipelines. Key improvements: absence paycode, pilot validation dashboards, and full JP site onboarding. Resulted in ~$2.5M annual savings and 3,300+ person-hours saved.(SQL/Datanet, S3/Redshift, QuickSight)- Automated ALPS plan generation across JP delivery stations, eliminating manual uploads. Reduced plan creation time to under an hour, ensuring on-time plan during peak holidays and resolving capacity constraints.(Python, XPath, Selenium, ALPS UI, VS Code)- Built dashboards to track absence/NRW defects across EU/JP, automating punch/VET/VTO/absence paycode checks, reducing manual audits by ~70%- Designed EU+JP shift-level audit framework validating Main Activity adherence; automated SQL checks reduced manual audits by 60–70%, enabling $1.4M in entitlement governance- Optimized LO Hub pipelines for EU/JP, consolidated staging tables, standardized timestamps, and automated ALPS→Octomate labor ingestion (~1,696 person-hours/year saved)- Added JP region, updated UI/backend for JP-specific metrics, enabling real-time data management and approvals across AMZL teams.(Python, Visual Studio, Docker)- Developed centralized Quicksight dashboards and JP-compliant Python scripts, unifying ALPS, SSPOT, and MyTime mappings; reduced manual errors by ~60%- GFS : Built centralized River Workflow and Quicksight dashboards for SIM/TT tracking, standardizing change management, improving resolution speed, and providing real-time visibility.(River, Quicksight, Lambda, S3, SQL, SOP Documentation)• LMAQ: Optimized transporter efficiency through flexible stop handling, achieving a 1.53% attrition reduction with 0.87s improvement in service time, surpassing cost-saving projections by 15.66m (A/B testing, AWS)

Education

• Amrita Vidyalayam

  Kochi

• National Institute of Technology Calicut

  Bachelor of Technology - BTech, Electrical and Electronics Engineering

• GEMS Education

  Our Own English High School, Sharjah

• University of Wisconsin-Madison

  Master of Science - MS, Business Analytics