Sanghamitra Saha

Business Analyst

Kolkata, West Bengal, India5 yrs experience

Highly Stable

Key Highlights

Over 4 years of experience in Data Analysis and Database Management.
Recognized with multiple awards for superior service quality.
Eager to embrace new challenges in Data Analytics and Machine Learning.

Stackforce AI infers this person is a Data Analyst with a strong foundation in Data Science and Machine Learning.

Contact

sanghamitrasaha.1091@gmail.com LinkedIn

Skills

Core Skills

Machine LearningPythonData Analysis

Other Skills

AS400ApplicationBusiness Intelligence ToolsC (Programming Language)C++Collaborative FilteringContent-Based FilteringCustomer Relationship Management (CRM)Customer Service ManagementData ManagementData ScienceData Science & AnalyticsDatabase AdministrationDatabase Management System (DBMS)Java

About

Qualified IT professional with more than 4 years of experience in Data Analysis, Database administration, Database Development, and Data Transformation across diverse projects. Currently working as Data Analyst and SME for 16 applications along with user access related issues from Admin perspective. Bagged spot award and multiple appreciation award from the client and Project Management in 2024, 2023, 2022 and 2021 for consistently rendering superior quality service across the assigned project phases. Possess an analytical mindset with decision-making ability and exceptional communication skills. At present I'm ready for another stage in my career, A new challenge. I like to switch and continue to grow and learn in Data Analytics/Science and Machine Learning field and take on some new tasks

Experience

Tata consultancy services

3 roles

Senior Analyst

Promoted

Mar 2022 – Aug 2024 · 2 yrs 5 mos

Data Processing Specialist

Promoted

Mar 2021 – Aug 2024 · 3 yrs 5 mos

Analyst

Aug 2019 – Mar 2022 · 2 yrs 7 mos

Ardent computech pvt ltd

Summer Industrial Training

Jun 2018 – Jul 2018 · 1 mo · India · On-site

Summer Industrial Training on MACHINE LEARNING WITH PYTHON
A movie recommendation system analyzes user preferences to suggest films. It often employs collaborative filtering, content-based filtering, or hybrid approaches. Collaborative filtering relies on user behavior, while content-based filtering considers movie attributes. Hybrid models combine both methods for better accuracy. Machine learning algorithms, such as matrix factorization or deep learning, play a role in predicting user preferences. Additionally, features like genre, director, and actors contribute to personalized recommendations.

Machine LearningPython