Shivaling Battarki Hazardous9hub

⛽ Enterprise Operations
350+ Retail Outlets
_{Monitored daily sales & stock at BPCL}

🔍 Cloud Relational SQL
99K+ Rows Analyzed
_{Target BigQuery multi-table case study}

🏆 Database Competency
5-Star Gold Badge
_{HackerRank verified SQL ranking}

🎓 Academic Merit
8.67 CGPA
_{B.E. Mechanical Engineering (VTU)}

👨‍💻 About Me & My Journey

I am a Data Analyst with a foundational background in Mechanical Engineering and 1 year of on-ground enterprise operations experience at Bharat Petroleum Corporation Limited (BPCL).

⚙️ From Machines to Data Systems: My engineering education built my appreciation for structured problem solving and applied mathematics. At BPCL, I experienced firsthand how physical enterprise operations run on data—monitoring daily fuel sales, stock movements, and dealer credit risk across 350+ retail stations.
🛠️ Process Improvement in Action: Using SAP ECC, Advanced Excel, and VBA, I restructured manual daily reporting pipelines, reducing MIS compilation time by 70% (2.5 hours down to 25 minutes) and evaluated bank credit eligibility under the e-DFS facility for 50+ retail partners.
📈 Continuous Upskilling with Scaler DSML: To deepen my statistical and technical capability, I joined the Scaler Academy Data Science & Machine Learning (DSML) program. I work with SQL (BigQuery, MySQL), Python (Pandas, NumPy, Scipy, Seaborn, Matplotlib), Probability, Hypothesis Testing, and Tableau Public.
🎯 Current Goal: Seeking full-time Data Analyst / Business Analyst opportunities where I can apply rigorous analytical thinking to solve operational, retail, and product challenges.

💻 Interactive SQL Terminal (Candidate Profile Query)

-- Querying candidate table for analytical mindset and verified impact
SELECT 
    candidate_name,
    primary_focus,
    operational_experience,
    verified_certifications,
    availability
FROM candidate_profiles
WHERE candidate_id = 'Shivaling_Battarki'
  AND 'Pragmatic Problem Solver' IN (core_attributes);

/* Result Set:
+--------------------+------------------------------------+----------------------------+-------------------------------------+--------------------+
| candidate_name     | primary_focus                      | operational_experience     | verified_certifications             | availability       |
+--------------------+------------------------------------+----------------------------+-------------------------------------+--------------------+
| Shivaling Battarki | Data Analytics, BI & Applied Stats | 1 Year @ BPCL (Operations) | HackerRank 5★ Gold, Deloitte (Sim)  | AVAILABLE FOR HIRE |
+--------------------+------------------------------------+----------------------------+-------------------------------------+--------------------+
1 row in set (0.01 sec)
*/

🛠️ Technical Arsenal

Languages & Querying
Analytics & Statistics
BI & Data Visualization
Enterprise & Workflow

📂 Featured Analytical Case Studies

1. 🛒 Target E-Commerce: Logistics Disparity & Revenue Scaling (Google BigQuery)

The Business Challenge: Analyze Brazilian retail e-commerce logistics, monitor revenue velocity across territories, and detect fulfillment bottlenecks.
Methodology: Queried 99,000+ customer records across 6 relational tables using Google BigQuery; formulated Common Table Expressions (CTEs), multi-table joins, and window ranking functions.
Quantitative Finding: Identified a 3.5x regional delivery lead-time disparity between northern and southeastern territories, while proving regional hub expansion was needed to safeguard a 137% YoY revenue growth surge.

Key SQL Pattern:

WITH delivery_metrics AS (
  SELECT customer_state, AVG(DATE_DIFF(order_delivered_customer_date, order_purchase_timestamp, DAY)) as avg_days
  FROM `target.orders` JOIN `target.customers` USING(customer_id)
  GROUP BY customer_state
)
SELECT customer_state, avg_days, DENSE_RANK() OVER(ORDER BY avg_days DESC) as rank
FROM delivery_metrics;

Links: GitHub Repository • Case Study PDF • Live Portfolio

2. 🏃 AeroFit: Customer Behavioral Segmentation & Profiling (Python, EDA, Probability)

The Business Challenge: AeroFit wanted to identify distinct buyer characteristics across entry-level (KP281), mid-tier (KP481), and commercial (KP781) treadmills.
Methodology: Formulated 2-way contingency tables, computed conditional probabilities $P(\text{Product} \mid \text{Fitness Rating})$ versus $P(\text{Product} \mid \text{Gender/Age})$, and conducted bivariate distribution analysis.
Quantitative Finding: Customer self-rated fitness levels (rating 4–5) exhibited an 80%+ conditional probability for the premium KP781 treadmill, disproving the previous assumption that age and income alone drove purchasing tiers.

Key Python Snippet:

# Calculating conditional probability matrix
contingency_table = pd.crosstab(index=df['Fitness'], columns=df['Product'], normalize='index') * 100

Links: Jupyter Notebook • Case Study PDF • Tableau Dashboard

3. 📊 Superstore Executive Sales & Profitability Dashboard (Tableau Public)

The Business Challenge: Enterprise leadership lacked real-time visibility into negative-margin product categories, customer concentration risks, and discount impacts.
Methodology: Designed dynamic parameter-driven views in Tableau Public with interactive filters for Region, Category, and Segment; engineered Level of Detail (LOD) expressions and calculated fields.
Quantitative Finding: Tracked $733K+ in total sales and identified that discounting Tables in the Central region accounted for over 60% of total regional operating losses, enabling immediate policy throttling.
Live Interactive View: Tableau Public Dashboard • Summary Dashboard PDF

4. ⛽ BPCL Operations MIS & Dealer Credit Evaluation (Advanced Excel, VBA, SAP ECC)

The Operational Challenge: Manual, repetitive daily reconciliation of sales volumes, tank stock levels, and dealer credit risk from SAP ERP transaction logs.
Methodology: Built structured Excel MIS models utilizing XLOOKUP, dynamic Pivot Tables, and automated VBA macros; monitored credit limit calculations under the bank e-DFS facility across 50+ retail dealers.
Quantitative Impact: Reduced daily reporting time from 2.5 hours to 25 minutes (70% reduction in reporting friction) and coordinated on-ground engineering support for 37 new retail outlet commissionings.

5. 🛒 Walmart Consumer Behavior & Distribution Analysis (Python, Stats & CLT)

The Analytical Challenge: Examine purchase patterns across diverse demographics during peak shopping events and test population spending assumptions.
Methodology: Applied Central Limit Theorem (CLT) sampling distributions, confidence interval estimations, and statistical hypothesis testing across demographic cohorts (gender, marital status, age brackets, city tiers).
Links: Jupyter Notebook • Case Study PDF

6. 🏭 Deloitte Australia: Daikibo Factory Telemetry & Downtime Analysis (IoT & Tableau)

The Business Challenge: Analyze machine telemetry data (temperature, vibrations, cycle rates) across factory floors to reduce unscheduled machine downtime.
Methodology: Explored and sanitized 60MB+ of industrial IoT telemetry logs; synthesized machine downtime metrics and delivered an executive operational dashboard.
Links: Deloitte Australia Certificate • Telemetry Dashboard

📜 Verified Certifications & Credentials

🏆 HackerRank SQL - 5-Star Gold Badge (View Verified Profile)
🇦🇺 Data Analytics Job Simulation — Deloitte Australia (via Forage)
🎓 SQL Skill Mastery Certification — Scaler Academy & InterviewBit
📊 Data Analytics & Visualisation: Probability & Statistics — Scaler DSML
🐍 Data Analytics & Visualisation: Python Libraries (Pandas, NumPy, Seaborn) — Scaler DSML
📈 Tableau & Excel Specialization — Scaler DSML
🎓 Bachelor of Engineering (B.E.) in Mechanical Engineering — VTU Belagavi (CGPA: 8.67)

📊 GitHub Activity & Problem Solving

🐍 GitHub Contribution Snake

💬 Analytical Mindset & Philosophy

"Numbers have an important story to tell. They rely on you to give them a clear and convincing voice."
— Stephen Few

Whether it's untangling daily sales across 350 petroleum stations, diagnosing regional fulfillment delays in BigQuery, or finding non-obvious purchase drivers with conditional probability, I enjoy the craft of making data clean, clear, and actionable.

_{Designed & Maintained with curiosity by Shivaling Battarki • Available for Data & Business Analyst Roles}

Navigation Menu