Tania G.

Product Manager

3 yrs 3 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Dynamic data engineer with a focus on compliance and tech innovation.
Published research on neural networks for semantic analysis.
Strong leadership skills in data engineering projects.

Stackforce AI infers this person is a Data Engineer with a strong focus on AI/ML and compliance solutions.

Contact

Skills

Core Skills

Data EngineeringData AnalysisMachine LearningData Science

Other Skills

Analog ElectronicsAnalytical SkillsApache AirflowApple SafariApple iCloudC++CommunicationComputer ArchitectureComputer GraphicsComputer OrganizationCreativity SkillsData StructuresDecision-MakingDigital ElectronicsDiscrete Mathematics

About

A dynamic data engineer and product leader blending the flavors of compliance, due diligence, and tech innovation to build smarter, risk-aware solutions.

Experience

3 yrs 3 mos

Total Experience

1 yr 1 mo

Average Tenure

2 yrs 2 mos

Current Experience

Kyc hub

3 roles

CS & Technical Product Manager

Sep 2025 – Present · 9 mos

Technical Product Manager

Promoted

Dec 2024 – Present · 1 yr 6 mos

AML Compliance, Risk Mitigation, and CDD Screening Solutions.

Data Engineer

Apr 2024 – Present · 2 yrs 2 mos

Data EngineeringTeam LeadershipTableauAnalytical SkillsApache AirflowData Analysis

Microchip technology inc.

Software Engineer Intern

Jul 2023 – Mar 2024 · 8 mos · Chennai, Tamil Nadu, India · On-site

Connectbud

Teacher

Jan 2023 – Jun 2023 · 5 mos · California, United States

Intellectyx, inc.

Data Science Intern

Oct 2022 – Jun 2023 · 8 mos · Bengaluru, Karnataka, India · Remote

Published an research paper titled as :- Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset
The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models’ performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP.

Machine LearningData ScienceNatural Language Processing (NLP)Semantic AnalysisPython (Programming Language)Data Analysis