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Data Science with less than a year in Machine Learning, NLP, and Computer Vision.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Data Scientist with hands-on experience in Machine Learning, NLP and Computer Vision. Skilled in Python, SQL and building end-to-end ML/DL pipelines for solving real-world AI problems.
Galgotias University
MCA (AI & ML) · AI & ML
N/A – Present
ICFAI University
BCA
N/A – June 30, 2023
D.A.V Public School
Senior Secondary
N/A – May 31, 2020
D.A.V Public School
Secondary
N/A – May 31, 2018
Naresh IT
Data Science Trainee
June 1, 2026 – Present
India
House Price Prediction
June 1, 2026 – Present
Built regression model with R2 score of 0.87 and deployed using Flask.
Customer Segmentation
June 1, 2026 – Present
Applied K-Means clustering and generated insights for customer targeting.
Object Detection (YOLO)
June 1, 2026 – Present
Developed real-time object detection using YOLO and OpenCV.
AI Chatbot (NLP)
June 1, 2026 – Present
Built chatbot using pretrained BERT model for automated support.
Full-Stack Data Science
Naresh IT
August 19, 2025 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a breadth of interest across different Data Science applications (computer vision, NLP, traditional ML). The 'Data Science Trainee' role and 'Full-Stack Data Science' certification align well with a Data Science target role. However, all projects are personal, and the experience is an internship, which limits the assessment of collaboration and real-world team dynamics. The pursuit of an MCA in AI & ML shows initiative and a learning-oriented mindset.
Soft Skills & Operational Fit
The candidate lists 'Problem Solving, Analytical Thinking, Communication, Teamwork' as soft skills. While these are crucial for a Data Scientist, there is no assessment data to validate these claims. The project descriptions are concise but lack detail on challenges faced or specific contributions, making it difficult to assess operational fit beyond technical skills.