Talent's Information
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Location
Indore, India
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Rate
$10.0 per Hour
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Experience
3 Year
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Languages Known
English,Hindi
Available for
Background Verification
40 Hr/Week
About Teena A
“3+ years of Experienced Data Scientist with a strong background in data analysis, machine learning, and statistical modeling. Accomplished in designing and implementing data-driven solutions to solve complex business problems and drive actionable insights. Expert in delivering end to end model building, exporting and integrating in production environments. Excellent skills in feature engineering and performing EDAs to input meaningful data points to models”
Tech Stack Expertise
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Python
Python,TensorFlow
1 Years
Work Experience
Data Scientist
- January 2019 - June 2023 - 4 Year
- India
Projects
Stock Price Predictor
- February 2023 - May 2023 - 4 Months
Technologies
Role & Responsibility
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The project was based on Time Series Sequence Prediction with Machine Learning and reinforcement learning. The main goal was to predict future prices (for the next consecutive day) for popular stocks. In this project, we collected and pre-processed historical stock market data, including price, volume, and market indicators, to create an environment for training the reinforcement learning agent.
Designed and trained a deep reinforcement learning model, such as Deep Q-Network (DQN) or Proximal Policy Optimization (PPO), to learn optimal trading strategies by maximizing rewards and minimizing risk. The app empowers users to make more informed investment decisions. It offers insights into potential price movements, helping users assess whether to buy, sell, or hold stocks. By predicting stock prices, the app can identify potential risks and enable users to take appropriate actions to mitigate them.
Responsibilities:
Gathering knowledge of the domain to understand and perform EDA.
Design and developed data pipelines, and fetching data from National Stock Exchange API.
Developed and implemented a reinforcement learning model for stock price prediction, leveraging. the power of artificial intelligence to make data-driven trading decisions.
The design of the AI model was inspired by Transformer Architecture, which has generated state of the art performances on Sequence Prediction in Natural Language Processing.
Statistical analysis and predictive modeling.
Data visualization and reporting.
NFT Tool
- July 2022 - December 2022 - 6 Months
Technologies
Role & Responsibility
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The project was based on NFTs. Among various tasks, one was to compare similarities between two NFTs that come from different resources and platforms. In this project, users can maintain a portfolio in which they can collect unique NFTs.
For this task, we create an API with the help of FastAPI which returns a similarity score based on two NFTs properties. We analyze the NFT's properties and decide on the threshold for the similarity score. If the score is under the threshold, then the user will keep the NFT in his portfolio otherwise he will drop the NFT.
Responsibilities:
Involved in designing, developing, and deploying the tool which can compare two NFT images from different sources and returns a score based on NFT properties.
Implement Inbuilt MobileNet Model with the help of Keras.
Built API Using FastAPI for upload images from different resources.
Hybrid Work Culture Analysis
- September 2021 - March 2022 - 7 Months
Technologies
Role & Responsibility
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The project was based on analyzing and finding the impact of the online and offline hybrid work culture on employees' satisfaction, happiness, performance, etc. We had to do correlation analysis and perform various hypothesis tests and data visualization techniques to analyze the collected data and identify key trends, and unhide patterns. Evaluated existing hybrid work policies and practices, benchmarking against industry best practices and emerging trends.Enhanced employee satisfaction by 25% through the identification and implementation of targeted initiatives to address challenges associated with the hybrid work model. Improved productivity metrics by 15% by optimizing communication tools, collaboration platforms, and remote work infrastructure.
Responsibilities:
Gathered the knowledge of the domain to understand and perform EDA.
Statistical analysis and hypothesis testing.
Data visualization and reporting.
Data collection, quantitative and qualitative analysis.
Analyzed the data and drew inferences at a 5% significance level. We drew different assumptions and applied a hypothesis test to check the validity of the assumptions.
Insurance Fraud Detection
- June 2020 - June 2021 - 13 Months
Technologies
Role & Responsibility
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The project was based on detecting fraudulent transactions in the insurance industry with the help of the ML algorithm. The project has used data analysis and machine learning techniques to extract valuable insights from vast amounts of insurance data. Applied statistical analysis and predictive modeling to assess fraud risk, identify trends, and develop proactive strategies for fraud prevention.
These insights can help users understand fraud patterns, identify emerging trends, and make informed decisions to improve fraud prevention strategies in the future. Created data visualizations and reports to communicate findings and support decision-making processes for fraud detection and mitigation.
Responsibilities:
Gathered the knowledge of the domain to understand and perform EDA.
Insurance fraud detection and investigation.
Data analysis and manipulation using Python and SQL.
Machine learning algorithms (classification, anomaly detection).
Statistical analysis and predictive modeling.
Data visualization and reporting.
Built a classification ML model to predict which transactions could be fraudulent with high accuracy. A comparative study has been conducted to decide which classifier is best for this project to train the behavior features of normal and abnormal transactions.
Soft Skills
Industry Expertise
Education
in Bachelor of Science in Statistics
Indore University- June 2017 - June 2020
