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Rajat
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Rajat

Toronto, Canada --:--:--

Rajat is a results-oriented and versatile Data Engineer / Machine Learning Engineer (MLOps) with 5+ years of work experience in writing ETL and data pipelines, and deploying ML models.  He is talented in solving real-world challenges with business & analytical acumen.  He has experience in strategy, business process & operations across industries. Rajat is proficient with open source tools, APIs & handling all stages of data science lifecycle: problem definition, extract data, EDA, data transformation, feature engineering & modeling to improve performance.  He has strong hands-on experience in Python, SQL, Tableau, Spark, Docker, AWS & GCP cloud.  He has also coached analysts and fellow data scientists in Data Analytics, Big Data, engineering best practices, and quantitative modeling techniques

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Skills
Years
1
2
3
4
5
6
7
8
9
10+
MongoDB
GCP
Spark
Machine Learning
Python
AWS
Git
Snowflake
MLOps
NLP
IntelliJ
SQL Server
Tensorflow
Xgboost
Jupyter
Scala
Airflow
Random Forests
Zeppelin
Azkaban
Flask
Data Studio
AI
Jenkins
MySQL
Docker
R
PowerBI
Tableau
Apache
Excel
Hadoop
PowerPoint
Glue
Java
Kafka
Unix
NLP
Developer Personality

Independent

Collaborative

Trailblazer

Conservative

Generalist

Specialist

Planner

Doer

Idealist

Pragmatist

Abstraction

Control

100
50
0
50
100
Feature Experience

Data ingestion / Data Extraction / ETL

Machine Learning

Data Science Life Cycle

CI/CD

MODERATE
EXTENSIVE
EXPERT
Cultural Experience

Fintech

Automobile

Oil and Gas

MODERATE
EXTENSIVE
EXPERT
Portfolio

HBC

Machine Learning Engineer/ Data Engineer

Categories

Work Experience : 2019-present
  • Leading and mentoring a team of MLEs and Data Scientists to complete various Data Science Projects
  • Leveraging machine learning for solving retail problems like – Customer Segmentation, Forecasting Demand, Pricing/ Promotion Analytics
  • Introducing the best practices for implementing Data Science techniques, Version Control, Data Science engineering, automation, etc.
  • Communicating with the business and leadership to understand the exact business problems to be solved using data
  • Introducing MLOps best practices for model deployment, model monitoring, alerts, etc,.,
  • Automating the infrastructure through AWS cloud formation / Terraform and serverless deployments.

Select Environment & Tools Used: Python, Spark, Scala, Azkaban/ Airflow, SQL, Tableau, AWS (Sagemaker, EMR, EC2, EKS), Docker, Kubernetes

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Paytm Labs

Data Engineer

Categories

Work Experience : 2019-2020
  • Created Large Volume Data Pipelines using Spark for several use cases. Used mix of tools like: Spark, Scala, Azkaban, etc. to finish and automate the flows. Wrote ETL pipelines for signal processing.
  • Used Airflow / Azkaban to orchestrate the data flows to submit the spark jobs to EMR / Glue.
  • Optimized spark jobs to reduce the resource utilization and improve efficiency
  • Developed and productionized a CTR prediction model using Python to forecast the CTR’s of different properties on the web and app.
  • Developed and deployed the gender prediction model to improve the known gender by 25%
  • Performed feature engineering and made the features available to various teams for campaign optimization purposes.
  • Tableau Visualizations: Created dynamic dashboards to depict actionable insights from Analytics

Select Environment & Tools Used: Spark, Scala, Python, Azkaban/ Airflow, SQL, Data Studio, CI/CD (Jenkins), Docker

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Accenture

Data Scientist

Work Experience : 2015-2019
  • Customer and Site Segmentation: Implemented various end to end solutions for clustering problems
  • Applied Machine Learning algorithms to predict the likelihood of customers responding to a campaign and to launch nationwide customer loyalty campaigns
  • Churn / Upsell Models: Built the predictive models to reduce churn and upsell premium products
  • Pricing and Promotion Analytics: Used time series techniques to find out the impact of price changes for different set of customers
  • Text Analytics: Sentiment Analysis and Topic Modelling to identify the drivers of various products using the customer reviews
  • Implemented Market Basket Analysis, Network Analysis, SKU Assortment for a retail client
  • Trained & compared results using various linear, boosting and stacking models: XGBoost, Random Forest and Linear algorithms with tuned hyperparameters for each SKU
  • Trained Technical Sales team on Technical & Soft Skills: AI for Business, Problem Solving, Communication Skills, Collaborative Problem Solving, Creative Thinking, Prototyping
  • Helped set up ETL pipelines and Data Lake for an automobile client
  • Applied knowledge of ML and AI techniques for Sales Datasets
  • Used topic modelling (NLP) to automatically classify reviews into various topics and then used sentiment analytics on the top of it to ascertain public’s opinion for that sku and topic.
  • Automated several manual workflows saving considerable time for supply chain and marketing analysts.
  • Regularly presented Data Science capabilities, research, and analysis at the CEO level.

 

Environment & Tools: SQL / Database, R, Python, Tableau, Advanced Excel, PowerPoint, Java, Unix

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