<ul><li>Worked on various data science analytics projects which include developing and implementing ML and AI models, developing pricing tools and dashboards for senior management and actionable insights for business.</li><li>Experienced in business use cases across industries, such as insurance, banking, pharma/life sciences, energy conservation, retail outlets, supply chain, taxation fraud and FMCG industries, rural development, and aviation.</li><li>Used machine learning algorithms such as such as Linear and Logistic Regression, KNN, Clustering (K-means), Naive Bayes Theorem, Principal Component Analysis, Support Vector Machines (SVM), GBM, Recommendation System, Computer Vision, and NLP.</li><li>Tools and technologies: Advanced SAS (Base SAS Certified), SQL, Tableau, VBA, RStudio and Python, MS Azure, AWS, Databricks, EMBLEM, RADAR (TowerWatson software), and MS Office.</li></ul>

Data Scientist

<ul><li>Worked on data science and analytics; developing predictive machine learning models and dashboards for visualization.</li><li>Developed five-year AI plan for one of the clients in Dubai.</li><li>Analyzed and developed Tableau dashboards for PM schemes.</li><li>Worked on GST e-Way Bill Analysis, which included fraud analytics to identify tax evasion for goods movement.</li><li>Developed customer retention model (US) for motor insurance to predict retention probability.</li><li>Built an NLP model to shortlist resumes for CV screening saving time and finances. </li></ul>

Technical Lead cum Manager

<ul><li>Developed ML models, dashboards, and pricing models for insurance.</li><li>Managed a team of three with their project delivery.</li><li>Developed risk premium models for motor and property.</li><li>Built predictive motor pricing models using ML algorithms. </li><li>Led model development, validation, implementation, and monitoring the market performance.</li><li>Simulated the pricing of the current vs. estimated model.</li></ul>

Actuarial Consultant & Assistant Manager

<ul><li>Worked on claims analytics, dashboards and pricing models for insurance.</li><li>Developed a logistic model to predict customer retention.</li><li>Identified brokers for sales opportunities.</li><li>Worked on Motor Loss Ratio Recovery (UK); accident and UW year-based LRs, claim frequencies and severity by perils.</li></ul>

Senior Consultant, Data Analytics

Case 1 - Market Mix Model for Pharma Client

Mohit Bansal

Scheme 2

Scheme 1

Fraud Analytics

Academic ePortfolio which highlights my academic projects and achievements in the PGP-AIML-Online program.                 To view in detail, go to https://eportfolio.mygreatlearning.com/mohit-bansal

Academic ePortfolio of Mohit Bansal for PGP-AIML-Online Program

Naive Bayes Classifier

Case 3 - Merchant Analysis Customer Segmentation

Case 2 - Presentation PDF - Insurance Case Study

English (British)

Mohit Bansal is a data scientist with mixed modeling experience. Over the past 10 years, he has worked for top global brands like PwC, AXA, and Allianz Insurance Group, where he has gained expertise in predictive modeling, machine learning, artificial intelligence, business analytics, and data engineering. He also has skills in algorithms such as Linear and Logistic Regression, KNN, Clustering (K-means), Naive Bayes Theorem, Principal Component Analysis, Support Vector Machines (SVM), GBM, Recommendation System, Computer Vision, and NLP, as well as with tools like Power BI, VBA, and Tableau.

Mohit Bansal

Employment Highlights

Education Highlights

Portfolio

Case 1 - Market Mix Model for Pharma Client

Scheme 2

Scheme 1

Fraud Analytics

Academic ePortfolio of Mohit Bansal for PGP-AIML-Online Program

Naive Bayes Classifier

Case 1 - Market Mix Model for Pharma Client

Scheme 2

Scheme 1

Fraud Analytics

Academic ePortfolio of Mohit Bansal for PGP-AIML-Online Program

Naive Bayes Classifier