Abhishek Singh

DATA ANALYST

Pune

Summary

Results-driven Data Analyst with over 2.5 years of experience utilizing SQL, Python, Power BI, and Tableau to convert intricate datasets into actionable business insights. Proficient in exploratory data analysis, statistical modeling, and dashboard development. Demonstrated ability to streamline ETL workflows and automate reporting processes. Proven track record of identifying trends to optimize performance and improve business outcomes. Passionate about enabling organizations to make informed, data-driven decisions.

Overview

years of professional experience

years of post-secondary education

Certification

Work History

Data Research Analyst

YDP Global Solutions Pvt. Ltd.

07.2024 - 04.2025

Analyzed and validated large-scale datasets (100K+ rows) in Excel, producing insights that supported strategic and operational decisions.
Streamlined manual data processing by building automated Excel workflows using advanced formulas, conditional logic, and PivotTables — reducing effort by 30%.
Designed and maintained real-time Power BI dashboards that visualized key metrics, improving KPI visibility across departments.
Conducted recurring and ad-hoc analysis to uncover performance gaps and deliver actionable, data-driven recommendations.
Worked closely with stakeholders to identify analytical needs and deliver custom Excel-based solutions for reporting and tracking.
Consolidated fragmented CRM lead data into a unified dataset using Excel (VLOOKUP, Power Query), increasing data accuracy by 25% and boosting email deliverability by 10%.

Research Analyst – Demand Generation

OnDirect

12.2021 - 09.2023

Cleaned and structured large contact datasets using formula-driven spreadsheets, enabling consistent outreach and better list segmentation.
Improved CRM data quality by introducing regular validation checks and data hygiene processes, raising accuracy and reducing duplication.
Developed automated reporting tools to track campaign metrics and performance trends, enhancing visibility for management teams.
Conducted segmentation and trend analysis, increasing targeting precision by 15% and improving follow-up efficiency.
Collaborated across departments to translate insights into process improvements aligned with campaign and business objectives.
Consolidated data from multiple sources into unified reporting formats, cutting preparation time and accelerating decision cycles.

Education

Bachelor of Science (B.Sc.) - Computer Science

Savitribai Phule Pune University

01.2019 - 01.2022

Skills

Python
SQL
Power BI
Tableau
Excel

EDA
Data Cleaning
ETL
Statistical Analysis
ML (K-Means)

Websites

github.com/abhiii-ds0011

Certification

PG Program in Data Science, AlmaBetter x IIT Guwahati, In Progress, 2024–2025

Additional Information

Retail Customer Segmentation – Unsupervised Machine Learning Case Study Tools: Python (Pandas, Scikit-learn, Matplotlib, Seaborn), Jupyter Notebook Dataset: Online Retail

Addressed inefficient “one-size-fits-all” retail marketing by implementing behavioral-based customer segmentation.
Preprocessed raw transactional data (~50K records), handled missing CustomerIDs, removed invalid entries, and engineered RFM (Recency, Frequency, Monetary) features.
Scaled features using StandardScaler to normalize value ranges ahead of clustering.
Performed EDA using histograms, pair plots, and heatmaps to identify natural clustering potential.
Applied K-Means Clustering and optimized K=4 using both Elbow Method and Silhouette Score.
Interpreted clusters into segments such as Loyal Customers, At-Risk Customers, and New Customers.
Delivered final output (customer_segments_data.csv) with RFM scores and cluster assignments for actionable marketing use.
Proposed tailored strategies including retention campaigns, onboarding flows, and upsell opportunities.

Business Statistics & Applied Analytics Tools: Excel, Python (NumPy, SciPy, Matplotlib), Power BI

Solved real-world business problems using applied statistical techniques and exploratory data insights.
Conducted Hypothesis Testing, ANOVA, and Linear Regression to evaluate operational impact and performance metrics.
Built visualizations in Excel and Power BI, including correlation matrices, distribution charts, and confidence interval graphs.
Assessed statistical significance of trends, enabling clearer cause-effect understanding for stakeholders.
Delivered executive-ready reports that translated complex analyses into clear, data-backed strategic recommendations.

Timeline