Raj Gupta
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Analyst with 1+ years in Data Analytics & Cloud Platforms
Results-driven Data Analyst and aspiring ML Engineer with a strong foundation in Mechanical Engineering and hands-on expertise in data analytics, cloud platforms, and AI-driven solutions. Proven ability to translate complex datasets into actionable insights, delivering measurable improvements in operational efficiency (20%), dashboard adoption (30%), and reporting accuracy (15%). Adept at bridging the gap between engineering domain knowledge and modern data science methodologies. Certified in Google Cloud, Oracle Cloud, and ServiceNow, with practical experience in Python, SQL, Power BI, Tableau, TensorFlow, and Generative AI frameworks. Passionate about building intelligent data pipelines and AI-powered tools that drive smarter business decisions.
Oriental Institute of Technology & Science
Master of Technology · Mechanical Engineering
August 1, 2023 – June 30, 2025
Lakshmi Narain College of Technology Excellence
Bachelor of Technology · Mechanical Engineering
August 1, 2019 – June 30, 2023
Tensor Solutions Tech Private Limited
Data Analyst
December 1, 2024 – December 1, 2025
Bhopal, Madhya Pradesh, India
Global Food Production Trend Analysis
June 1, 2026 – Present
Analyzed multi-decade global food production datasets from FAO using Python (Pandas, NumPy) to identify regional production trends, yield gaps, and climate-correlated anomalies across 180+ countries. Built interactive Tableau dashboards visualizing crop-specific trends, enabling policymakers to identify at-risk food supply regions and prioritize resource allocation. Applied time-series forecasting (ARIMA) to project production trends for key staple crops, achieving 92% forecast accuracy on validation datasets.
Engiquery: AI Technical Manual Assistant
June 1, 2026 – Present
Designed and developed an AI-powered Q&A assistant that enables engineers to query dense technical manuals and maintenance documentation using natural language, powered by Generative AI and Retrieval-Augmented Generation (RAG). Integrated document parsing (PDFs, Word files) with vector embeddings and a semantic search engine, reducing manual lookup time for engineers by over 60%. Deployed on Google Cloud Platform with a REST API backend, enabling scalable access across engineering teams.
Google Cloud Data Analytics Certification
Google Cloud
June 1, 2026 – Present
Google Cloud Generative AI
Google Cloud
June 1, 2026 – Present
Oracle Cloud Infrastructure (OCI) Foundations
Oracle
June 1, 2026 – Present
ServiceNow Certified System Administrator
ServiceNow
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from global food production analysis to an AI technical manual assistant, showcases adaptability and a broad interest in applying data science to different domains. Their academic background in Mechanical Engineering, combined with a specialization in data analytics, indicates an interdisciplinary approach and a willingness to bridge traditional engineering with modern data methodologies. The certifications in various cloud platforms and AI also suggest a proactive learning attitude and alignment with a culture of continuous improvement and technological adoption. The target role of Data Analyst aligns well with their professional experience and project work.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, problem-solving, cross-functional collaboration, and stakeholder communication skills through project descriptions and work experience. Their ability to translate complex datasets into actionable insights and improve operational efficiency aligns well with a data-driven organizational culture. The experience in defining and tracking KPIs and implementing data validation pipelines suggests a detail-oriented and quality-focused approach to data operations.