Aryan Sinha

AI Researcher

Bengaluru, Karnataka, India9 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Developed innovative AI solutions for data transformation.
Achieved Kaggle medal for exceptional sentiment analysis project.
Presented research at NASSCOM AI Conference 2026.

Stackforce AI infers this person is a Data Scientist with expertise in AI and Machine Learning for enterprise solutions.

Contact

aryansinharv2003@gmail.com LinkedIn

Skills

Core Skills

Machine Learning

Other Skills

Lang graphLangChainData scienceRNNCNNWord2VecHugging FaceApache SparkPrompt EngineeringDockerPinecone.ioRetrieval-Augmented Generation (RAG)Hugging Face ProductsChatbot DevelopmentGPT-4

About

As a data scientist ,I am enthusiastic about creating intelligent systems that transform data into insightful knowledge. I like working at the nexus of machine learning, artificial intelligence, and problem solving, with a strong emphasis on comprehending ideas thoroughly and using them to solve practical problems. I am constantly experimenting with new concepts, experimenting with contemporary tools, and learning how to create effective, scalable, and useful solutions. Also I am looking to collaborate with researchers (Both from Academia and Industry) to collaborate and impactful research work at top tier conferences.

Experience

9 mos

Total Experience

9 mos

Average Tenure

9 mos

Current Experience

Sigmoid

3 roles

Associate Data Scientist

Jan 2026 – Present · 4 mos · Bengaluru

Also built an ambiguity detection and reformulation agent to be incorporated in the pipeline.
The idea was also presented at NASSCOM AI Conference 2026.

Data Science Trainee

Jul 2025 – Dec 2025 · 5 mos · Bengaluru

Currently involved working on Internal Accelerator Development.
Worked with building Knowledge Layer for structured dataset. Built an entity detection and mapper.

Lang graphLangChain

Data Science Intern

Jan 2025 – Jun 2025 · 5 mos · Bengaluru

Was assigned a Tweet sentiment analysis project (Sentiment 140 dataset) and implemented a custom RNN + CNN combination architecture of minimal layers with Word2Vec embeddings imitating attention mechanism at a very nuanced level for maximum performance optimisation without significantly increasing no of trainable parameters and model runtime. Obtained a medal for Kaggle notebook submission as well. Also used Hugging Face for running embedding models like Qwen (600 Million paramters) ,E5-Base/Large-V2 based on MTEB scoreboard and also used fine-tuned BERT models like RoBERTa , BERTweet.
Scored the highest marks in entire Intern batch.

Data scienceMachine Learning