PROFILE

Senior data & ML analyst with 8+ years across fintech, insurance and travel. Developed & deployed ML/LLM solutions for pricing, customer retention, NPS and content quality, delivering ?10M+ impact, playing a key role in cross-functional projects and advising C-suite stakeholders.

TECHNICAL & LEADERSHIP EXPERTISE

• Technical: SQL, Python (pandas,scikit-learn), dbt, Airflow, AWS (S3, Athena, Redshift), Git, REST APIs
• AI/ML: XGBoost, LightGBM, logistic/linear models, A/B testing, TensorFlow, LLMs (RAG, embeddings, evaluation), Transformers
• BI: Looker (LookML), Tableau, Jupyter Notebook, Excel
• Leadership: hiring & mentorship, cross-functional delivery, C-suite communication, cost & risk analytics

EXPERIENCE

2022 - Present

Role: Senior Data Analyst 

Customer: HOLIDU

Tasks:

Managed, mentored & hired analysts | Travel Tech

Apartment quality & photo ranking

• Developed quality signal for partners (photos, reviews, facilities, min-stay), kept separate from price/availability; evaluated on CTR, CTB, and normalised Net eGMV
• Implemented an OpenAI CLIP photo score (0?100) for the apartments and re-ranked the main photo
• Designed and ran A/B experiments focused on CTR and CTB, confirming higher click-through rates while maintaining stable conversion to booking, which drove a +4% lift in net eGMV

NPS text analytics with LLMs

• Developed an NPS analysis pipeline using Python, OpenAI, Redshift, S3, DuckDB, and Airflow
• Reduced manual effort by 70% in analysing 200+ NPS visualisations, enabling faster detection of customer pain points

Data modelling, dashboarding & experimentation

• Designed end-to-end data pipelines in Airflow using a medallion architecture (S3 ? dbt ? Redshift), with data quality checks and automated alerts via Opsgenie and Slack
• Developed and maintained dashboards in Looker covering key company metrics, host and guest behavior patterns, and financial reporting, supporting both operational and strategic decision-making
• AB test infrastructure: Built a Redshift?S3 data setup for the guest team to track past and ongoing A/B tests, giving clear visibility into metrics like value per user, GMV per user, session trends, and experiment significance (p-values)

Churn prediction & pricing experiments

• Built and deployed an XGBoost churn model (AUC 0.81) with automated scoring and triggers, identifying the top 4% highest-risk hosts
• Implemented targeted credit interventions validated through A/B testing, reducing churn by 10% and retaining ~?1M+ in annual revenue.

2020 - 2022

Role: Team Lead Data Analyst

Customer: ACKO

Tasks:

Managed & hired analysts and data scientists | Insurance Tech

Dynamic Pricing Optimisation -?5M+ Revenue Growth

• Developed a lightGBM pricing elasticity model (AUC=0.72) that increased premiums by 6% (~?5M+/year) while maintaining conversion rates
• Ran A/B experiments to validate dynamic pricing strategies against traditional rule-based methods

Loss ratio improvement:

Identified high-risk segments (e.g., fuel type, website behavior,pincode) to cut loss ratios by 12%.

2018 - 2020

Role: Decision Analyst 

Customer: KABBAGE (Acquired by American Express)

Tasks:

USA?s lending fintech

• Analysed and optimised underwriting strategies, cutting bad debt by 9% through granular risk segmentation (industry, FICO, line bands) and refining qualification criteria
• Built logistic regression and LightGBM models (AUC 0.68) to predict 90-day customer activation, driving an 8% uplift in onboarding rates via targeted campaigns

2017 - 2018

Role: Decision Scientist

Customer: MU-SIGMA

Tasks:

Fortune 500 analytics consulting | Retail/CPG focus

• Worked on Azure ETL migration, cutting infrastructure costs by 22% and enhancing reporting efficiency