Gopesh Khandelwal

AI Researcher

West Delhi, Delhi, India5 yrs 2 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

5+ years of experience in data science and analytics.
Expert in building predictive models and delivering actionable insights.
Proficient in advanced AI techniques and tools.

Stackforce AI infers this person is a Data Science expert in AI and analytics-driven solutions.

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Skills

Core Skills

Data ScienceMachine LearningArtificial Intelligence

Other Skills

LangchainGenerative AIStatistical ModelingStatisticsAWSDeep LearningC++VisualizationPyTorchNetworkXLinuxBashGitRecommender SystemsPySpark

About

Data Scientist with 5+ years of experience in data science, consulting, and advanced analytics. Experienced at creating data regression/classification models, using predictive modeling, analyzing data to deliver insights, and implementing action-oriented solutions to complex business problems like customer segmentation, salesforce sizing, marketing mix, propensity modeling, recommendation engine, fraud detection, and forecasting. Proficient in Machine Learning, Deep Learning, Natural Language Processing, Statistical Modeling, Python, and SQL.

Experience

5 yrs 2 mos

Total Experience

2 yrs 7 mos

Average Tenure

3 yrs 5 mos

Current Experience

Fractal

2 roles

Senior Data Scientist

Promoted

Apr 2025 – Present · 1 yr 2 mos · Hybrid

Data Scientist II

Jan 2023 – Apr 2025 · 2 yrs 3 mos · Hybrid

LangchainGenerative AIData ScienceMachine Learning

Zomato

Data Scientist

Sep 2022 – Dec 2022 · 3 mos · Gurugram, Haryana, India · On-site

Machine LearningStatistical ModelingData Science

Zs

Data Science Associate

Dec 2020 – Sep 2022 · 1 yr 9 mos · New Delhi, Delhi, India · Hybrid

Machine LearningStatisticsData Science

Bobble ai

2 roles

Artificial Intelligence Engineer

Sep 2020 – Nov 2020 · 2 mos

Building on-device ASR Systems (end-to-end Speech to Text) for Indian languages - Hindi and Marathi, for Bobble Keyboard and Xiomi Mint Keyboard - OEM, with an SER of 28%.
Building data pipelines connecting AWS and in-house GPU servers to efficiently fetch and feed data to DL models for training, model development and deployment, data cleaning, and optimization.
Implementing architectures from research papers such as ConvGLU, Seq2Seq+TDS,wav2seq+lexicon free, and Nemo TDS end-to-end ASR; in frameworks wav2letter (facebook AI Research) and nemo(NVidia).
Building an internal platform for rapid beta-testing of models and inference pipelines using Flask.