Jaswinder S.

AI Researcher

Bengaluru, Karnataka, India3 yrs 6 mos experience

Key Highlights

Expert in developing predictive models for financial insights.
Proficient in machine learning and data processing with PySpark.
Strong background in test automation and quality assurance.

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

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Skills

Core Skills

Data ScienceMachine LearningTest AutomationDeep Learning

Other Skills

API testingAgile MethodologiesAgile Project ManagementAnalytical SkillsC (Programming Language)C++Cloud ComputingComputer ScienceComputer VisionConvolutional Neural NetworksCypressDAXData AnalysisData AnalyticsData Loading

About

Data Enthusiast | Aspiring Quant I’m a data-driven problem solver passionate about turning complex datasets into impactful financial insights. Transitioning from Test Automation to Data Science and now into quantitative finance, I’m pursuing a Master’s in Financial Engineering at WorldQuant University, mastering Python, SQL, PySpark, and machine learning to decode market trends and build robust models. 🌟 My Journey Briskcovey Technologies: Built predictive models and ETL pipelines with PySpark to process blockchain data, slashing computation time with optimized workflows. Cisco: Automated QA with Cypress, enhanced cloud portal performance, and collaborated to deliver seamless user experiences. ISRO: Engineered Python-based data pipelines and GUI tools to analyze satellite imagery, boosting regional data accuracy. 📚 What I’m Exploring Quantitative finance, time-series forecasting, and anomaly detection across financial markets Deep learning (CNNs, transfer learning) for cutting-edge data applications. Data storytelling to drive strategic decisions. When I’m not wrangling data or debugging a model into submission, I’m probably deep in a book, because nothing pairs better with financial volatility than a strong coffee and a plot twist I didn’t have to backtest. Let’s connect and talk data, finance, or your favorite book recommendation.

Experience

3 yrs 6 mos

Total Experience

1 yr 9 mos

Average Tenure

Current Experience

Briskcovey technologies

Analyst Data Science

Oct 2022 – Apr 2025 · 2 yrs 6 mos · Jaipur, Rajasthan, India · Hybrid

I tackled complex blockchain data challenges by fusing data science and analytics to drive impactful insights. Leveraging Python and scikit-learn, I developed machine learning models to detect transaction anomalies, while streamlining data preprocessing with pandas and NumPy, cutting processing time by 70%. I processed 10+ TB of data using PySpark, reducing computation times from hours to minutes through optimized distributed workflows. Additionally, I built interactive Power BI dashboards using DAX, visualizing blockchain metrics in real time and boosting stakeholder engagement through automated, insight-driven reporting.

Pythonscikit-learnPySparkpandasNumPyPower BI+3

Cisco

Test Automation Engineer

Aug 2021 – Aug 2022 · 1 yr · Bengaluru, Karnataka, India

Demonstrated proficiency in API testing, crafting comprehensive test cases, and submitting pull requests via Git.
Utilized automation testing tools such as Cypress and Selenium to streamline testing processes and ensure product quality.
Cultivated expertise in JavaScript and Cypress, enabling me to contribute significantly to cross-functional teams and improve project outcomes.

CypressSeleniumJavaScriptAPI testingGitTest Automation

Chegg inc.

Subject Matter Expert

Mar 2021 – Nov 2022 · 1 yr 8 mos

Space applications centre, isro

Trainee Research Development

Jan 2020 – Jun 2020 · 5 mos · Ahmedabad, India

Utilized the HDF5 library in Python for efficient storage and manipulation of large INSAT-3D and INSAT-3DR satellite image datasets during preprocessing.
Implemented NumPy for numerical computations and array operations to enhance the accuracy of land surface temperature analysis.
Applied K-nearest neighbors (KNN) algorithm to extract relevant temperature data from satellite imagery with high precision.
Collaborated with a multidisciplinary team to validate the accuracy of temperature analysis results using statistical methods and machine learning techniques.
Documented the entire process using word ensuring comprehensive and accessible documentation for better evaluation and understanding.
Engaged in continuous learning and professional development to stay updated with the latest advancements in remote sensing, image processing techniques, and Python libraries.

HDF5NumPyK-nearest neighborsPythonData Science

Bennett university

Deep Learning & AI Intern

Dec 2018 – Jan 2019 · 1 mo · Greater Noida, Uttar Pradesh

Used convolutional neural networks (CNNs) and other cutting-edge deep learning techniques to categorise photos with human faces, achieving a strong 60% validation accuracy.
Employed transfer learning in conjunction with pre-trained CNN architectures, such ResNet and VGG16, to enhance classification precision and minimise training duration.
Used data augmentation methods to increase the diversity of the training dataset and boost the generalisation of the model, including rotation, scaling, and flipping.
Adjusted hyperparameters, including batch size, dropout rate, and learning rate, to maximise model performance and get higher validation accuracy.
Performed error analysis to find prevalent misclassifications and adjust training methods and model architecture accordingly.
To promote repeatability and knowledge sharing, the complete process—which includes dataset preparation, model training, hyperparameter tuning, and assessment metrics—was documented.
Presented findings and insights from the image classification task to stakeholders, highlighting the model's performance, limitations, and potential applications.

Convolutional Neural Networkstransfer learningdata augmentationDeep Learning