Akash Kumar

AI Researcher

Samastipur, Bihar, India11 mos experience

Key Highlights

Created a real-time inventory dashboard improving decision-making by 30%
Developed a hybrid model achieving 92% accuracy in air quality forecasting
Built a stock price prediction model with LSTM achieving 84% accuracy

Stackforce AI infers this person is a Data Science and Machine Learning professional with a focus on Fintech and Data Engineering.

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Skills

Core Skills

Machine LearningData EngineeringData AnalysisEtlStatistical AnalysisData Science

Other Skills

Extract, Transform, Load (ETL)XGBoostNeural NetworksLong Short-term Memory (LSTM)MatplotlibKerastensorflowsadamreluIT Business AnalysisStatistical Data AnalysisSeleniumMicrosoft ExcelPython (Programming Language)Microsoft Power BI

Experience

11 mos

Total Experience

11 mos

Average Tenure

11 mos

Current Experience

Turing

AI,ML Engineer

Jun 2025 – Present · 11 mos · New York, United States · Remote

Extract, Transform, Load (ETL)Data EngineeringXGBoostNeural NetworksLong Short-term Memory (LSTM)Matplotlib+33

Peerhire

Data Analyst

May 2024 – Jun 2024 · 1 mo

Created a real-time inventory freshness dashboard using Python, and LookerStudio, significantly enhancing decision-making accuracy by 30%
Designed Python-based ETL automation, reducing inventory reporting processing time by 99% and enabling instant SKU-level analysis
Automated retention data processing with MoEngage API integration, performed SHA-256 encryption and enhanced data accuracy by 90%
Used Selenium for influencer metrics scraping, reducing selection time by 90%, enhancing interaction analysis, and marketing effectiveness
Implemented K-means clustering for customer segmentation, enhancing targeted marketing and personalized product recommendations

IT Business AnalysisStatistical Data AnalysisPythonLookerStudioSeleniumK-means clustering+3

Indian institute of technology, kharagpur

Summer Research Intern

May 2023 – Jul 2023 · 2 mos · Kharagpur, West Bengal, India · On-site

Performed stationarity analysis and smoothening after visualizing using matplotlib on Kanpur air quality data for time-series forecasting
Developed a hybrid model using ML models like MLP, LSTM neural network, and XGBoost regressor to forecast PM2.5 concentration
Achieved a maximum prediction accuracy of PM2.5 concentrations with notably high R2 score of 0.92 and low RMSE loss of 12(µg/m3)

Statistical Data AnalysisLong Short-term Memory (LSTM)XGBoostMatplotlibMachine LearningStatistical Analysis

Jpmorganchase

Data Science virtual Internship

Apr 2023 – May 2023 · 1 mo · Remote

Built a robust stock price prediction model using LSTM neural networks with TensorFlow Keras and trained the model for 100 epochs
Used Matplotlib, and Seaborn to identify significant trends, patterns, and correlations in the financial stock data, improving model accuracy
Fine-tuned the LSTM model, using the Adam optimizer, Relu activation, and MSE loss function to achieve a notably high R2 score of 0.84

LSTMTensorFlowKerasMatplotlibSeabornadam+3