Japsehaj Singh Wahi

+1-414-***-**** ad4anw@r.postjobfree.com linkedin.com/in/japsehaj-singh Dallas, TX EDUCATION

University of Texas at Dallas, Texas Aug 2022-May 2024 M.S. in Business Analytics with specialization in Data Science GPA: 4.0/4.0 Courses: Applied Machine Learning, Deep Learning, NLP, Big Data, Advance Statistics, Web Analytics, Business Analytics, Predictive Analytics, Prescriptive Analytics, Database Foundations for Business Analytics Jaypee Institute of Information Technology, Noida, India Jul 2015-May 2019 B.E. in Computer Science

SKILLS

Programming Languages: Python (NumPy, Pandas, Scikit-learn, PyTorch, PySpark, TensorFlow, ggplot, Matplotlib), R, SQL, C++, Spark Tools: Jupyter Notebook, Tableau, Jira, Git, Databricks, Dataiku DSS, Alteryx, VBA, MS Excel, Hadoop, Snowflake, Redshift, Java Techniques: Statistical Data Analysis, Machine Learning (Linear Regression, Logistic Regression, Classification, Decision Tree, Clustering), Reinforcement Learning, Deep Learning, Time Series Analysis, Data Visualization, NLP Certifications: Python for Data Science, Google Analytics (Nov 2022-Nov 2023), Alteryx Micro-Credential WORK EXPERIENCE

Trinity Industries, Dallas, United States May 2023-Dec 2023 Data Science Intern (Python, SQL, Databricks, AWS)

• Developed a custom geofencing solution for train stations using DBscan clustering to track the train's current location.

• Conducted data engineering and preprocessing techniques to improve data quality and usability for forecasting models.

• Analyzed data and created interactive Tableau visualizations for actionable insights and data-driven decision-making. ZS Associates, Gurugram, India Jun 2019-Jul 2022

Decision Analytics Associate Consultant (Python, SQL, Tableau, PyTorch, DSS) Jan 2022-Jul 2022

• Successfully led a team of 3 associates and established strong client relationships, providing expert solutions to complex business challenges for Fortune 500 pharmaceutical clients.

• Created customer engagement score to analyze the performance of the revamped website for a pharmaceutical client; increasing the revenue by 30% through target marketing.

• Spearheaded multiple Machine Learning projects to uncover key revenue drivers and shape effective business strategy.

• Onboarded new hires to the team and provided client-specific training: conducted regular one-to-one feedback meetings. Decision Analytics Associate Jun 2019-Dec 2021

• Built tableau dashboards to provide insights on drug switching patterns, competitor performance, drug adherence, compliance, etc. on patient-level claims data in SQL and R.

• Developed statistical models using decision tree and XGBoost algorithm to predict sales for a rare disease drug, achieving an accuracy of 90%.

• Conducted segmentation and targeting analysis for the vaccines business unit in Python to identify priority accounts for sales representatives, resulting in a 5% increase in revenue.

• Optimized deliverables using VBA and Python alongside automating redundant work during ad-hoc requests: reducing the operation time by 30%.

• Developed an automated tool (a user-triggered link between Jupyter R notebook and Tableau) for a client to ensure standard KPI reporting at scale: reducing the turnaround time by 25%. PROJECT EXPERIENCE

Credit Risk Model UT Dallas Jan 2023-Jun 2023

• Predicted the probability of default for a customer by developing various machine learning models – Decision Tree, Logistic Regression and Neural Network; Decision tree had the highest AUC score of 96%.

• Defined two strategies conservative and aggressive by setting a threshold to accept or reject the applicant based on the model’s output. Calculated the revenue and default rate for both strategies. Neural Network Implementation from Scratch UT Dallas Jan 2023-Jun 2023

• Implemented a Neural Network on PyTorch, both with and without Autograd, and subsequently analyzed loss and accuracy plots to compare results.

Analysis of Air Quality and Impacts on Human Health Phase 1 Jan 2018-Jun 2018

• Forecasting the AQI for the subsequent week utilizing various machine learning models - Multiple Linear Regression, Random Forest, and Artificial Neural Network- compared their results to find the ideal model yielding an accuracy of 82%.

• Paper was published as a chapter in ‘Smart Healthcare Systems’, a book by CRC Press, Taylor and Francis Group (Link). Analysis of Air Quality and Impacts on Human Health Phase 2 Jan 2019-May 2019

• Utilized the Phase 1 results and users' input regarding their symptoms. Designed a deep learning model with H2O library in R to predict the disease, resulting in an accuracy of 70% (Link).