Akshat Saxena

Business Development Executive

Gurugram, Haryana, India1 yr 8 mos experience

Key Highlights

Developed machine learning models for sea level rise predictions.
Improved solar panel efficiency by 20% through innovative algorithms.
Strong analytical skills demonstrated in data-driven projects.

Stackforce AI infers this person is a Data Analyst with expertise in Environmental Science and Renewable Energy.

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Skills

Core Skills

Data AnalysisData EngineeringMachine Learning

Other Skills

Attention to DetailData AnalyticsPython (Programming Language)SQLData VisualizationRegression AnalysisAzure DatabricksComputer ScienceData PipelinesData ArchitectureSatellite CommunicationsOceanographyRemote SensingStatistical Data AnalysisR Shiny

Experience

1 yr 8 mos

Total Experience

1 yr 8 mos

Average Tenure

1 yr 8 mos

Current Experience

Blinkit

Data Analyst

Sep 2024 – Present · 1 yr 8 mos · Gurugram, Haryana, India · On-site

Attention to DetailData AnalysisData EngineeringData AnalyticsPython (Programming Language)SQL

Space applications centre, isro

Research Intern

Feb 2024 – Jun 2024 · 4 mos · Ahmedabad, Gujarat, India · On-site

Analyzed sea level rise predictions up to the year 2100 from various international space institutes, comparing them with actual satellite data. Utilized xarray to compute around 80 datasets, each containing 10 million data points.
Developed machine learning models using regression analysis and random forest in scikit-learn and TensorFlow to predict global mean sea level rise.
Created detailed projection reports using data visualization tools like matplotlib and geoplotlib.

Python (Programming Language)Machine LearningData AnalysisData VisualizationRegression Analysis

Renew

Data Science Intern

Jun 2023 – Aug 2023 · 2 mos · Gurugram, Haryana, India · On-site

Created a soiling loss simulation algorithm that increased power efficiency by 20% while reducing the cleaning costs of solar panels.
Achieved this result by working on Azure Databricks using Python libraries such as PySpark and Pandas.
Worked on approximately 40 datasets, each containing 50,000 data points, while collaborating with a team of 15.

Python (Programming Language)Machine LearningData AnalysisData EngineeringAzure Databricks