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Priyansh Mahajan

Data Scientist | Credit Risk, Fraud & Investment Analytics | Product & Growth Analytics | Customer Retention, CLV & Experimentation | SQL, Python, ML | 4+ YOE

Mumbai

5+ years of work experience

PI Industries Ltd
Aug 20261 moPresent

Mumbai

Kotak Securities
Mar 20242 y 4 moJul 2026

Mumbai

Developed time-series forecasting models (EMA, ARIMA, SARIMA) to predict brokerage trends, improving stakeholder decision-making accuracy by 25%. Built and led the development of interactive product dashboards using Streamlit, tracking SIPIT and Stockcase performance metrics and enabling real-time decision-making without reliance on BI tools. Defined and owned the product metrics framework, including retention, engagement, and conversion funnels, analyzing 50K+ transactions/month to identify key drivers of user activation and repeat usage. Designed and led A/B testing experiments and campaign strategies, applying statistical significance testing to evaluate impact on conversion and onboarding, driving ~3% month-on-month growth in SIPIT adoption. Drove a 12% increase in MTF product usage by leading data-driven analysis and translating behavioral insights into actionable strategies across offline distribution channels. Architected advanced customer segmentation and persona models based on demographic and transaction behavior, increasing customer engagement by 17–25% and significantly improving personalization and repeat purchases. Led funnel analysis initiatives to identify transaction drop-offs (~15–20%) and implemented targeted interventions, improving order success rates by 8–12%. Delivered high-impact financial and customer insights, contributing to a 10% increase in market share and retention of ~$3.5M in revenue. Developed and deployed KNN-based recommendation models to deliver personalized investment strategies, improving customer preference understanding by 40% and enhancing overall client engagement. Led automation of PowerPoint reporting workflows using Python, reducing creation time from 3 days to 1 day (~67% efficiency gain) and saving ~$5K/month in tool costs. Collaborated with cross-functional stakeholders (product, business, and operations teams) and influenced data-driven product and strategic decisions across multiple products ecosystems.
Piramal Capital & Housing Finance Limited
Jul 20239 mosMar 2024

Hybrid

Led development of XGBoost-based fraud detection models, improving fraud detection accuracy by 30% and enabling more effective risk mitigation. Designed and deployed real-time Power BI dashboards to monitor fraud metrics, improving decision-making efficiency by 40% across multiple business units. Co-developed a CNN-based face verification model, enhancing security protocols with 92% accuracy and strengthening fraud prevention mechanisms. Collaborated with engineering and cross-functional teams to optimize data pipelines and improve the scalability and performance of fraud detection systems. Applied NLP-based topic modeling to extract insights from unstructured documents, reducing manual processing time by 50% and improving efficiency in fraud sampling for internal and external stakeholders.
Piramal Capital & Housing Finance Limited
Jul 20221 yr 1 moJul 2023

On-site

Identified and implemented data-driven strategies to reduce loan rejection rates from 6.2% to 3%, while maintaining a stable fraud rate of ~6%, using exploratory and descriptive analytics combined with business insights. Developed and maintained fraud monitoring dashboards using Power BI, SQL, and Excel, enabling real-time tracking of fraud trends and improving alignment between analytics and business teams. Designed and enhanced fraud scoring frameworks, improving risk assessment accuracy and supporting more informed loan approval decisions. Built secure, role-based Power BI dashboards for the Fraud Control Unit with row-level security (RLS), ensuring data confidentiality while improving operational efficiency and reducing manual effort. Created network visualizations of account transactions, mapping relationships between entities to detect hidden fraud patterns and suspicious linkages. Performed bank statement analysis to detect anomalies and irregular transaction patterns, strengthening fraud detection capabilities. Developed and applied NLP-based topic modeling to extract insights from unstructured data, improving fraud investigation efficiency. Co-developed a CNN-based face verification model, enhancing identity validation processes and strengthening fraud prevention mechanisms. Designed and implemented fraud detection rules and trigger systems, improving early identification of high-risk applications and reducing exposure to potential fraud.
SpringML, Inc.
Jan 20227 mosJul 2022

Hyderabad

Explored and implemented key services on Google Cloud Platform (GCP), gaining hands-on experience with cloud-based data and AI solutions. Applied cloud security best practices to ensure secure data handling and compliance within GCP environments. Worked with DocAI, BigQuery, Dataflow, Vision AI, and AutoML, building solutions that improved processing efficiency and reduced turnaround time for data workflows. Assisted in developing data processing and analytics pipelines, leveraging GCP tools to handle large-scale datasets effectively. Collaborated with cross-functional teams to deliver solutions aligned with business requirements and project goals. Identified technical and operational issues, performed root cause analysis, and proposed data-driven solutions to improve system performance. Applied analytical thinking and problem-solving skills to break down complex problems and support decision-making processes. Consistently executed tasks with high accuracy and efficiency, contributing to timely delivery of project milestones.
TheRightDoctors
Aug 20215 mosDec 2021

Hyderabad

Analyzed the recruitment testing process, identifying inefficiencies in a workflow requiring ~6 hours of manual effort per job opening. Designed and implemented automation for recruitment testing workflows, reducing execution time from 6 hours to 2 hours (~66.7% reduction) and significantly improving operational efficiency. Conducted A/B-style analysis on email subject lines, increasing email click-through rates by 23% and improving candidate engagement. Researched and optimized email autoresponder strategies, enhancing response rates and overall outreach effectiveness. Performed lead generation through manual research and web scraping, contributing to the expansion of prospect data within Zoho CRM. Analyzed and documented Zoho Recruit workflows and processes, creating structured documentation that accelerated onboarding and reduced ramp-up time for new employees.
GlobalShala
Aug 20211 moAug 2021

Gurugram, Haryana, India

Entering the students data from their pdf and jpeg marksheet into the system.
GitHub
@PriyanshDragonHeart

Priyansh’s AI Stack

antigravity
antigravity
claude code
claude code
cursor
cursor
gemini
gemini
hugging face
hugging face
ChatGPT
ChatGPT

Education

Vellore Institute of Technology

Bachelor of Technology - BTech, Computer Science

Vellore Institute of Technology

20184 yrs2022

Higher Secondary Education, Physics, Chemistry, Maths

Sacred Heart Senior Secondary School - India

20152 yrs2017

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Headline

Data Scientist | Credit Risk, Fraud & Investment Analytics | Product & Growth Analytics | Customer Retention, CLV & Experimentation | SQL, Python, ML | 4+ YOE

About

Driven Data Scientist & Product Analyst with ~4 years of experience leveraging data to drive business growth, user engagement, and product performance in the financial services domain. I specialize in user behavior analysis, experimentation, and data-driven decision-making, with hands-on experience in cohort analysis, funnel optimization, and statistical modeling to improve retention, conversion, and revenue outcomes. At Kotak Securities, I have built end-to-end analytics solutions, including interactive dashboards using Streamlit, defined product metrics frameworks, and led experiment-driven growth initiatives that improved onboarding and engagement. My work has contributed to double-digit improvements in key product metrics and more efficient decision-making across teams. Previously, at Piramal Capital, I worked on fraud analytics and risk modeling, applying machine learning and statistical techniques to analyze large-scale transaction data, improve detection accuracy, and enable proactive risk management. I enjoy solving complex problems by combining analytical rigor with product intuition — focusing not just on what is happening, but why it’s happening and how it can be improved. Core areas of interest: Product Analytics & Growth Experimentation & A/B Testing Data Science & Machine Learning User Behavior & Customer Insights I’m currently open to Product Analyst, Data Scientist, Data Analyst, and AI-focused roles, especially in global and remote-first teams. Let’s connect if you’re looking to turn data into impactful product and business decisions.

Location

Mumbai

AI stack

antigravity

claude code

cursor

gemini

hugging face

ChatGPT

Work experience

Manager Data Scientist at PI Industries Ltd, Aug 2026 - Present · 1 mo, Mumbai

Manager - Data Scientist at Kotak Securities, Mar 2024 - Jul 2026 · 2 y 4 mo, Mumbai, Developed time-series forecasting models (EMA, ARIMA, SARIMA) to predict brokerage trends, improving stakeholder decision-making accuracy by 25%. Built and led the development of interactive product dashboards using Streamlit, tracking SIPIT and Stockcase performance metrics and enabling real-time decision-making without reliance on BI tools. Defined and owned the product metrics framework, including retention, engagement, and conversion funnels, analyzing 50K+ transactions/month to identify key drivers of user activation and repeat usage. Designed and led A/B testing experiments and campaign strategies, applying statistical significance testing to evaluate impact on conversion and onboarding, driving ~3% month-on-month growth in SIPIT adoption. Drove a 12% increase in MTF product usage by leading data-driven analysis and translating behavioral insights into actionable strategies across offline distribution channels. Architected advanced customer segmentation and persona models based on demographic and transaction behavior, increasing customer engagement by 17–25% and significantly improving personalization and repeat purchases. Led funnel analysis initiatives to identify transaction drop-offs (~15–20%) and implemented targeted interventions, improving order success rates by 8–12%. Delivered high-impact financial and customer insights, contributing to a 10% increase in market share and retention of ~$3.5M in revenue. Developed and deployed KNN-based recommendation models to deliver personalized investment strategies, improving customer preference understanding by 40% and enhancing overall client engagement. Led automation of PowerPoint reporting workflows using Python, reducing creation time from 3 days to 1 day (~67% efficiency gain) and saving ~$5K/month in tool costs. Collaborated with cross-functional stakeholders (product, business, and operations teams) and influenced data-driven product and strategic decisions across multiple products ecosystems.

Deputy Manager - Associate Data Scientist at Piramal Capital & Housing Finance Limited, Jul 2023 - Mar 2024 · 9 mos, Hybrid, Led development of XGBoost-based fraud detection models, improving fraud detection accuracy by 30% and enabling more effective risk mitigation. Designed and deployed real-time Power BI dashboards to monitor fraud metrics, improving decision-making efficiency by 40% across multiple business units. Co-developed a CNN-based face verification model, enhancing security protocols with 92% accuracy and strengthening fraud prevention mechanisms. Collaborated with engineering and cross-functional teams to optimize data pipelines and improve the scalability and performance of fraud detection systems. Applied NLP-based topic modeling to extract insights from unstructured documents, reducing manual processing time by 50% and improving efficiency in fraud sampling for internal and external stakeholders.

Graduate Engineering Trainee - Data Scientist at Piramal Capital & Housing Finance Limited, Jul 2022 - Jul 2023 · 1 yr 1 mo, On-site, Identified and implemented data-driven strategies to reduce loan rejection rates from 6.2% to 3%, while maintaining a stable fraud rate of ~6%, using exploratory and descriptive analytics combined with business insights. Developed and maintained fraud monitoring dashboards using Power BI, SQL, and Excel, enabling real-time tracking of fraud trends and improving alignment between analytics and business teams. Designed and enhanced fraud scoring frameworks, improving risk assessment accuracy and supporting more informed loan approval decisions. Built secure, role-based Power BI dashboards for the Fraud Control Unit with row-level security (RLS), ensuring data confidentiality while improving operational efficiency and reducing manual effort. Created network visualizations of account transactions, mapping relationships between entities to detect hidden fraud patterns and suspicious linkages. Performed bank statement analysis to detect anomalies and irregular transaction patterns, strengthening fraud detection capabilities. Developed and applied NLP-based topic modeling to extract insights from unstructured data, improving fraud investigation efficiency. Co-developed a CNN-based face verification model, enhancing identity validation processes and strengthening fraud prevention mechanisms. Designed and implemented fraud detection rules and trigger systems, improving early identification of high-risk applications and reducing exposure to potential fraud.

Consultant Data Engineer at SpringML, Inc., Jan 2022 - Jul 2022 · 7 mos, Hyderabad, Explored and implemented key services on Google Cloud Platform (GCP), gaining hands-on experience with cloud-based data and AI solutions. Applied cloud security best practices to ensure secure data handling and compliance within GCP environments. Worked with DocAI, BigQuery, Dataflow, Vision AI, and AutoML, building solutions that improved processing efficiency and reduced turnaround time for data workflows. Assisted in developing data processing and analytics pipelines, leveraging GCP tools to handle large-scale datasets effectively. Collaborated with cross-functional teams to deliver solutions aligned with business requirements and project goals. Identified technical and operational issues, performed root cause analysis, and proposed data-driven solutions to improve system performance. Applied analytical thinking and problem-solving skills to break down complex problems and support decision-making processes. Consistently executed tasks with high accuracy and efficiency, contributing to timely delivery of project milestones.

Business Analyst at TheRightDoctors, Aug 2021 - Dec 2021 · 5 mos, Hyderabad, Analyzed the recruitment testing process, identifying inefficiencies in a workflow requiring ~6 hours of manual effort per job opening. Designed and implemented automation for recruitment testing workflows, reducing execution time from 6 hours to 2 hours (~66.7% reduction) and significantly improving operational efficiency. Conducted A/B-style analysis on email subject lines, increasing email click-through rates by 23% and improving candidate engagement. Researched and optimized email autoresponder strategies, enhancing response rates and overall outreach effectiveness. Performed lead generation through manual research and web scraping, contributing to the expansion of prospect data within Zoho CRM. Analyzed and documented Zoho Recruit workflows and processes, creating structured documentation that accelerated onboarding and reduced ramp-up time for new employees.

Intern at Globalshala at GlobalShala, Aug 2021 - Aug 2021 · 1 mo, Gurugram, Haryana, India, Entering the students data from their pdf and jpeg marksheet into the system.

Education

Bachelor of Technology - BTech, Computer Science, Vellore Institute of Technology

Higher Secondary Education, Physics, Chemistry, Maths, Sacred Heart Senior Secondary School - India

Public links

github: https://github.com/PriyanshDragonHeart