Data Analyst → Data Scientist / ML Engineer

High-potential data professional with strong analytical fundamentals, quantified problem-solving proof, and hands-on experience delivering real-world, decision-driven analytics. I turn raw data into confident business decisions today while deliberately compounding toward scalable ML systems for tomorrow.

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I am an early-career data professional (0–1 years) with a strong bias toward structured problem solving and outcome-driven analytics. My core strength lies in combining SQL, Python, and statistics to solve realistic business problems under real constraints.

I have delivered end-to-end analytics projects across retail sales forecasting and inventory optimization, working with messy data, seasonality, and operational trade-offs.

Short term: High-impact Data Analyst
Long term: Data Scientist / ML Engineer at a top product-based company (NVIDIA-level)

Languages & Querying

• Python (Pandas, NumPy)
• SQL (Joins, CTEs, Subqueries, Window Functions)

Analytics & Data Science

• Data Cleaning & Feature Engineering
• Exploratory Data Analysis (EDA)
• Probability & Hypothesis Testing
• Machine Learning Fundamentals

Visualization & BI

• Matplotlib, Seaborn
• Power BI (Dashboards, KPI Tracking)

Execution Practices

• Metric-driven analysis
• Reproducible workflows
• Business-first framing

Problem: Inconsistent sales performance and weak demand visibility
Approach:

• Cleaned and standardized transactional data
• Analyzed trends, seasonality, and growth drivers
• Built forecasting models for future demand
  Impact:
• Enabled data-backed inventory and revenue planning
• Identified high-impact product categories and seasonal patterns

Problem: Inefficient stock utilization and unclear restocking signals
Approach:

• Analyzed sales velocity and stock aging
• Segmented inventory by movement behavior
• Designed operational KPIs
  Impact:
• Improved restocking and clearance decisions
• Increased inventory planning clarity

• Advanced SQL analytics and query optimization
• Machine learning depth (model evaluation, bias–variance trade-offs)
• Interview-grade DSA, SQL, and analytics problem-solving
• Resume-aligned, real-world analytics projects

• Numbers > narratives: Live problem counts, not vague claims
• Strong fundamentals: Statistics, SQL, Python at the core
• Low ramp-up risk: Real-world, end-to-end project execution
• High growth ceiling: Analyst → Data Scientist trajectory
• Ownership mindset: Structured, accountable, outcome-driven.

• GitHub: https://github.com/Abhayvishe
• LeetCode:https://leetcode.com/u/Abhay4126
• HackerRank:https://www.hackerrank.com/profile/abhayvishe4126
• LinkedIn: https://www.linkedin.com/in/abhay-vishe/

Open to **Data Analyst roles, analytics internships where fundamentals, execution, and long-term growth matter.