
Senior data scientist
Ramen Chetia 
Profile Summary
11 years of experience as IT professional with experience in ETL, Machine Learning, Deep Learning, Natural Language processing, Computer Vision and Campaign management
Total 6 years of relevant experience in Machine learning and deep learning.
Having good knowledge in Azure data factory, used it to extract the data from different sources and created one final dataset.
Involved in projects from data extraction, data manipulation, EDA, Model building and deployment.
Helped stakeholders in decision making by providing insights from the data. Pre-processing, EDA and Feature engineering
methods are used to develop models.
Strong hands-on experience with Python and Pyspark in applying Machine learning algorithms to prepare a model.
Hard working, highly motivated, and reliable individual with a constructive approach to the business, client and to other team members.
Good experience in developing data pipelines in Azure Data Factory to extract the data from source and apply transformations in the extracted data and store it in Blob storage.
Experience in predictive analytics, campaign management analytics and Fraud detection.
Experience on the implementation and maintenance of ML model, regular Hyper-parameter tuning is applied.
Well verse with Code version control systems like Git.
Worked with MLFlow architecture and it components, building a training pipeline with MLFlow.
Expertise on creating ML model with advanced algorithms like Random forest, XGBoost, LightGBM, OpenCV.
Experience in end to end ML model solution design, from extraction till model deployment.
Technical Skills
Application ADF, Databricks, Snowflake, Databricks AutoML
Programming Languages Python, Pyspark, R
IDE Databricks notebook, Jupyter Notebook, PyCharm
Database My SQL, Oracle DB
CVC, Container Git, Docker
Algorithms &amp;amp; Concepts Logistics regression, CART, Bagging, Boosting, NLP, CV
Tech in ML Regression, Classification analysis, NLP, CV, Recommender engine, Campaign management, CNN, 2D-LSTM, Tensorflow
PROFESSIONAL EXPERIENCE
Month-Year Designation or Role/Company name Role Responsibilities
Mar 2022
(Currently Working) Technical analyst (Data science)
(Coforge Pvt Ltd) Worked for Voya Financial to implement ML solutions
Day started with analysing the data and devising a solution to retrieve the hidden insights from data.
Build ML model, handling end to end process. Created pipeline to extract the data, work on the data, build a model and deploy the model in production
environment.
Migrating SPSS pipelines to python and Pyspark codes in Azure cloud and test it.
Handled Fraud detection and Customer engagement capabilities, implemented ML solutions in different use-case
Nov 2019 -Mar 2022 Data Scientist (Capgemini Malaysia Bhd.) Worked for RHB bank and implemented ML solutions to replace the rule based legacy systems.
Developing solutions for various departments where we have
implemented advance analytics and tried to find optimal solutions to boost the revenue growth.
From machine learning to deep learning, developed predictive ML solutions for different use-cases.
Discussion with stakeholders to understand the rule based systems that are being used and finding solutions to replace it with smart ML model.
Worked in advanced AI algorithms like Pytessaract to develop a model for OCR using OpenCV.
Worked in end to end solution development, data extraction to model deployment.
ETL Developer Creating data pipelines to extract, transform and load data into data warehousing environment
Designed and maintained data pipelines and debugged if any issue arises
Worked with different teams to deploy enhancement works.
Worked on POC&rsquo;s to apply machine learning algorithms to different data
Gained knowledge on Data pre-processing, exploratory data analysis, extracting valuable insights from data.
Applying Hyper-parameter tuning to re-train old models and update it.
Sep 2012&ndash; Oct 2017 (IBM India Pvt. ltd) Junior Data Scientist Dec 2017 &ndash; Nov 2019 (IBM India pvt. ltd) PROJECT EXPERIENCE
Project name: Managed account adoption for Voya Financial
Duration Feb , 2023, to Nov, 2023 Role Data Scientist
Project Type ML solution to replace legacy system Technology ML, Python, Pyspark, Azure, Databricks, SQL
My
Responsibilities Work with stakeholders to understand the work, devise some plan with client to apply ML solutions to it to make it smart and effective
Extract data from different sources, massage the data and create a final dataset, save it in blob storage or create a table to store the data.
Import the data to Databricks notebook and write python codes to pre-process, EDA and model development.
Use MLFlow to create pipeline and by using Docker to deploy the model.
Automate and integrate the process by using ADF pipeline.
While extracting the data, quality of the data are checked and verified. Source target mismatch, Invalid values, Null values, non-standard date formats are some of the examples of checks done.
After completing the pre-process, analysis of data is done, if any hidden patterns are found then they are visualized with different graphs to share it with client.
Feature selection is done with the help of business knowledge, ML algorithms. These features are extensively studied, new features are created with feature engineering if required.
Tableau is used to generate different graphs to analyse the pattern and distribution of data.
Different ML algorithms are used to develop the model and compare the performance, cross validation and hyper-parameter tuning is used to improve the performance of the model.
After multiple runs in production, the performance of the model is monitored and if any depletion of performance happen then re-training of the model is done.
Project name: Cross sell of health benefit products
Duration Mar 2022 to Dec 2023 Role Data Scientist
Project Type ML solution to replace legacy system Technology ML, Python, Pyspark, Azure, Databricks, SPSS
My
Responsibilities This is a migration project from SPSS to cloud.
While extracting the data, the quality of the data are checked and verified. Source target mismatch, Invalid values, Null values, non-standard date formats are some of the examples of checks done.
Comparing the source of the SPSS pipeline and dataset made in ADF to extract the data.
Created ML model after completing the pre-processing and exploratory data analysis, this model mimics the SPSS model, but performance is enhanced in cloud.
Tableau is used to generate different graphs to analyse the pattern and distribution of data.
An Auto ML model is created in data bricks to compare the performances of the ML model, along with that parameter, features are also compared.
Name of the Project: - Money mule detection (Fraud detection)
Duration Sep 2021&ndash; Feb 2022 Role Data Scientist
Project Type Fraud detection Analytics Technology ML, Python, Pyspark, Azure, SAS, SQL
My
Responsibilities This is a use-case for Anti money laundering department, money mules are the account holders of the bank which are being used to launder money. From the customer base, this use-case is to find the accounts which can be used as money mules.
By using ML solutions I have created a model to identify those money mule accounts which can be used to launder money.
Legacy system is used to study the features being used in it, the different source systems are identified.
While extracting the data, quality of the data are checked and verified. Source target mismatch, Invalid values, Null values, non-standard date formats are some of the examples of checks done.
Using SAS the required data is extracted and saved as blob storage files.
After completing the pre-process, analysis of data is done, if any hidden pattern are found then they are visualized with different graphs to share it with client.
Feature selection is done with the help of business knowledge, ML algorithms. These features are extensively studied, new features are created with feature engineering if required.
Python Matplotlib and Seaborn libraries is used to generate different graphs to analyse the pattern and distribution of data.
Different ML algorithms are used to develop the model and compare the performance, cross validation and hyper-parameter tuning is used to improve the performance of the model.
After modelling the model is deployed using Docker, MLFlow
Name of the Project: -Credit card forms scan
Duration Mar 2021&ndash; Aug 2021 Role Data Scientist
Project Type Handwritten character recognition Technology ML, Python, OpenCV, Tensorflow, CNN, 2D-LSTM
My
Responsibilities Project is for the department of GIA (Group Internal audit), task is to read and extract required information from scanned documents of credit card.
Some of the fields such as DoB, application number which is present in the credit card application form need to be extracted and saved in a table.
For this task Numpy, OpenCV, Tensorflow is used to pre-process the images, making it ready for Pytesseract to read the image file.
The data that is being extracted by Pytesseract is then processed from regex to extract the required information.
This task is also tried using CNN and 2D-LSTM, the result of this task is compared with the Pytesseract results.
All the image pre-processing like image threshold, changing image angle to remove the noise that are.
present in the images, also Augmentation process is followed during training.
National Institute of Engineering, Mysore
IIT - Kharagpur
MCA
Business Analytics and AI certification
EDUCATION
National Institute of Engineering, Mysore
IIT - Kharagpur
MCA
Business Analytics and AI certification
EDUCATION

Detailed IT Skills
Skill Name Exp. in Years Skill Name Exp. in Years Skills Exp. in Years
Languages Languages Operating Systems
Service Now ITSM Python 5 windows 10
Service Now ITOM Pyspark 3 Unix 1
Service now ITIL R 1 Big Data Platform Cloud Development Digital
Pyspark 2 Azure 2 DataBricks 2 Tools/ Platforms S/w Configuration tools Hardware
Git Hub 2 Azure Data Factory 2 --
Jenkins DataBricks AutoML 2 MLFlow 1 Testing Practice Domain
Banking &amp;amp; Finance 7
Insurance 2
Databases Retail 1
Oracle 6 SQL 6 