Harvinder Singh

AI Researcher

Gurugram, Haryana, India3 yrs 11 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Implemented an AI agent reducing manual effort by 80%
Led NLP pipeline for SaaS product SAMS
Developed REST APIs for NIMBUS product

Stackforce AI infers this person is a Data Scientist specializing in SaaS and Big Data solutions.

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Skills

Core Skills

Machine LearningNatural Language ProcessingBig DataWeb Development

Other Skills

Amazon Web Services (AWS)Anti-Money LaunderingCatBoostCompliance (GRC)Data AnalysisData VisualizationDjango REST FrameworkElastic SearchElasticsearchExtra Tree classifierGensimGitGovernanceKibanaLangChain

About

I am currently working as a Data Scientist in Solytics Partners. In my current role, I work on 2 SaaS based platform which utilizes Machine Learning and Natural Language Processing. I have worked on different part of ML pipeline which includes Variable Reduction, Model Training & Evaluation, Model Explainibility using LIME & SHAP, Baisness and Fairness. on NLP side, I have worked on Word2Vec models, NER, Sentiment Analysis, Transformer based model and Semantic Similarity using Spacy , transformer and gensim library. I have also knowledge of Time Series Analysis. I worked on different Time Series model i.e. ARIMA, ARIMAX, SARIMAX, ARDL and OLS. I have experience in working on big data using pyspark as well. I hold a Master Degree in Computer Science with specialization in Big Data Analytics fron Central University of Rajasthan. During my master's, I have taken the course on probability and statistics, linear algebra, machine learning, advanced statistical methods and econometrics & finance. My skill sets are following: Python, Machine Learning, Natural Language Processing, AWS, Django, PostgreSQL, Time Series Analysis, Elastic Search, Kibana, Git & GitHub, Docker, Pyspark, Transformers, Spacy, Gensim

Experience

3 yrs 11 mos

Total Experience

3 yrs 11 mos

Average Tenure

3 yrs 11 mos

Current Experience

Solytics partners

4 roles

Senior Data Scientist

Promoted

Apr 2024 – Present · 2 yrs 2 mos

Working on LLM Evaluation & Monitoring
Implemented a fully automated AI agent for Politically Exposed Person (PEP) profiling using LangGraph, LangChain, and OpenAI, with web search capabilities, reducing manual effort by 80%.

Data Scientist

Apr 2023 – Mar 2024 · 11 mos

Led the NLP pipeline for SaaS product SAMS
Currently working on enhancing Named Entity Recognition models using spacy library.
Worked on Sentiment Analysis using transformer based models.
Worked on Word2Vec and Sentence Transformer models to enhance the vector representation and Semantic Similarly results.

Natural Language Processing (NLP)Sentiment AnalysisPython (Programming Language)TransformersNamed Entity Recognition (NER)Gensim+2

Associate Data Scientist

May 2022 – Mar 2023 · 10 mos

Led three module of our largest SaaS product - NIMBUS.
Led the team of 7 members including Junior and interns.
Done code review.
Added support of Big Data in NIMBUS platform using Pyspark, pandas on spark and MLlib.
Added support of GLM and GEE models in Time Series Modelling.
Implemented Baisness and Fairness submodule for Model Interpretation.
Worked on different POCs for product's feature enhancement.
Developed a new Name Matching Algorithm for another SaaS product SAMS using fuzzy matching with enhanced accuracy and efficiency.
Interviewed and shortlisted candidates for recruitment drive.

Big DataKibanaPython (Programming Language)PySparkElasticsearchMachine Learning

Data Science Intern

Sep 2021 – Apr 2022 · 7 mos

Worked on our largest product - NIMBUS
Developed REST API using Django REST Framework.
Worked on different classification model technique i.e. XGBoost, CatBoost, Random Forest , Logistic Regression and Extra Tree classifier.
Added support of different Time Series Modelling techniques i.e. ARIMA, ARIMAX, ARDL, OLS Regression in NIMBUS
Worked on Model Explainibility of different classification models using SHAP & LIME
Used AWS S3 for storing and fetching the data and model.
Used celery and AWS SQS for async operation implementation.
Used Docker to containerized the application and deployed them using AWS ECS.