Akshay Sahni

Data Scientist

Pune, Maharashtra, India7 yrs 2 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Expert in big data analytics and NLP.
Proven track record in fraud detection and sentiment analysis.
Strong foundation in AI and machine learning methodologies.

Stackforce AI infers this person is a Data Scientist specializing in Fintech and advanced analytics.

Contact

Skills

Core Skills

Data ScienceMachine LearningData Management

Other Skills

Analytical SkillsArtificial Intelligence (AI)Attention to DetailAutomationBig Data AnalyticsCoding ExperienceCommunicationComputer ScienceData AnalyticsData Management SystemData VisualizationDecision TreeDeep LearningDeep Neural Networks (DNN)Fraud Detection

About

At EPAM Systems, our team leverages big data analytics and natural language processing to extract meaningful insights, significantly optimizing time through fine-tuned configurations. As a current Master of Technology candidate in Artificial Intelligence and Machine Learning at BITS Pilani, I'm enhancing my technical expertise, particularly in statistical modeling and deep learning. My experience at Tata Consultancy Services honed my abilities in system engineering, where I developed a data management system, later adopted across various platforms. Contributing to projects involving sentiment analysis and fraud detection, I have consistently demonstrated an attention to detail and a commitment to innovation in data science.

Experience

Epam systems

2 roles

Senior Data Scientist

Promoted

Oct 2025 – Present · 5 mos · Pune, Maharashtra, India

Data Scientist

Mar 2022 – Oct 2025 · 3 yrs 7 mos · Pune, Maharashtra, India

Tata consultancy services

System Engineer

Jan 2019 – Mar 2022 · 3 yrs 2 mos · Pune Area, India

Market Surveillance
Calculations on Big Data for various user stories using Pyspark which helped in time optimized results by fine-tuning spark configurations.
Extracted insights of emails by utilizing various Natural Language Processing techniques to preprocess data and then applied pre-trained BERT Sentiment, BERT Summary, and Topic Modelling in a distributed manner to improve time performance for millions of records.
Developed Data Management System which was generic for all the RDBMS, Object Relational Method was made use of, which was later re-utilized by different offerings as well.
To identify the fraudulent banks and relation between different features in a dataset, Decision Tree and Random Forrest Classifier were implemented which predicted alerts at Precision (81,93) and Recall (96,73).
For simplifying the analysis of data to detect and alert the malicious activity, an advanced Network Graph was developed using the email dataset which helped in Cluster Analysis.
Applied K-means Clustering Algorithm and Louvain Algorithm to perform further cluster analysis on the network Graph.
Built various graphs using data visualization libraries for the understanding of data and designed a pipeline for data preprocessing.
EQA Sheet Automation (POC)
Proposed and implemented an automation tool that generates a report which identifies all the errors from the activity log file.
The report consists of daily and weekly errors committed by users in the form of bar graphs and line charts for quality inspection.
The report is then mailed automatically to the common Mail group for analysis, therefore, reducing more than 80% of work when done manually.
To store the past records, RDBMS was used where configuration file with DB connections and table schemas were created. It helped in setting up the database at ease during deployment of
application.