Kunal Sawant

AI Researcher

Mumbai, Maharashtra, India9 yrs 11 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

5+ years of experience in Machine Learning and Data Science.
Expertise in Python, AWS, and Spark for data-driven solutions.
Proven track record in developing fraud detection and recommendation systems.

Stackforce AI infers this person is a Machine Learning Engineer with a strong background in Fintech and Data Science.

Contact

Skills

Core Skills

Machine LearningApi Development

Other Skills

XGboost Classifierscikit-learnFlaskSHAPEnsemble ModelsDjangofuzzywuzzyData StructuresJavaAndroid DevelopmentCC++Oracle DatabaseMongoDBAgile

About

Professional Machine Learning Engineer with 5+ years of experience working in the financial and consumer services industry. Expertise in coding in Python and strong exposure to working with AWS and Spark. Experience of working with relational and non-relational databases and machine learning frameworks like Tensorflow, Keras, and Scikit-learn.

Experience

9 yrs 11 mos

Total Experience

3 yrs 3 mos

Average Tenure

4 yrs 2 mos

Current Experience

Meesho

2 roles

Data Scientist 3

Jul 2025 – Present · 11 mos

Price Debiasing in rankers ( High ASP )
In-session Personalisation

Data Scientist II

Apr 2022 – Jul 2025 · 3 yrs 3 mos

Working on Personalisation
Previously worked on
New catalog discovery ( Supplier Growth )
Recommendation engine ( Cold start problem )
Ads ( new catalogs )
Price sensitive Recommendations

Servify

2 roles

Senior Machine Learning Engineer

Promoted

Mar 2021 – Apr 2022 · 1 yr 1 mo

1. Developed a Fraud detection pre-purchase model using XGboost Classifier and scikit-learn which helps company to catch fraudulent cases right at the time of purchase.
2. Designed an API using Flask to interpret the model using SHAP library which provides which features had more impact while deciding whether something is fraud or not.

XGboost Classifierscikit-learnFlaskSHAPMachine LearningAPI Development

Machine Learning Engineer

Aug 2019 – Mar 2021 · 1 yr 7 mos

1. Created a service centre cost forecasting and risk assessment model using Ensemble of various models which forecasts the cost which will be incurred by the service centre to out company in the next 3 months based on previous patterns.
2.Provided business team with EDA on High Risk service centres from historical data and why they are high risk like the link between retailer and service centre and the type of plans they were buying.
3.Developed an API using Django and libraries like fuzzywuzzy to identify whether the phone given by customer is original phone or a fake phone and also reduced the response time from 10 seconds to 150ms using Caching .

Ensemble ModelsDjangofuzzywuzzyMachine LearningAPI Development

Jpmorgan chase & co.

2 roles

Associate

Promoted

Dec 2018 – Aug 2019 · 8 mos

1. Developed an application which helps users to to collect data from multiple data sources like excel, SQL databases, scraping emails / excels and then generates different reports to onboard into python based application which in turn removes the manual intervention needed during their process.
2.Improved throughput of the application by 90 % using distributed compute system named (Compute BackBone) and DAG Framework named DG2.

Application Developer

Jul 2016 – Dec 2018 · 2 yrs 5 mos

1.RWA Recalc ( JP Morgan ) Optimisation - Python:
Decreased the response time from 1 hr to 10 minutes by designing a queuing system . which used to divide the large task into batches then used to send the small task through the queue.
2.SFT Calculator
Added an feature in the application which showed the trend in last six months of data and then showed them on Desktop App using chart by using pychart and matplotlib library