Akhil Lohia, Developer in Bengaluru, Karnataka, India
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Akhil Lohia

Verified Expert  in Engineering

Data Analytics Developer

Bengaluru, Karnataka, India
Toptal Member Since
November 23, 2018

Akhil is a data scientist and economist by training with experience across academia and corporate projects. He has modeled large volumes of customer clickstream data for end-to-end machine learning pipelines using Spark and Python as well as census, questionnaire, and RCT data in a research setting. He communicates extremely well and has worked with teams across time zones. Akhil is also adept at picking up new skills quickly.


Python, Amazon Web Services (AWS), Deep Learning, Machine Learning...
MYRM Technologies, LLC
Pandas, Salesforce, Matching Systems, Jupyter Notebook, Amazon Athena...
Amazon SageMaker, PyTorch, Amazon Web Services (AWS), PySpark, Data Science...




Preferred Environment

Python, Git, PyCharm, Jupyter, Unix

The most amazing...

...project I've worked on was a customer support chatbot for the largest online travel agency in India.

Work Experience

Senior Data Scientist

2020 - 2021
  • Developed a module that extracts relevant information from medical documents such as prescriptions, pathology lab reports, and vaccination certificates and makes them digitally available and searchable.
  • Used LayoutLM model to exploit position and to extract the key terms in medical documents.
  • Developed end-to-end pipeline from uploading documents to entity extraction, including document classification and manual data annotation steps on AWS ecosystem.
  • Collaborated on designing medically relevant hierarchies for different medical conditions and symptoms using SNOMED CT, which helped provide contextual options to doctors in their prescription pad.
Technologies: Python, Amazon Web Services (AWS), Deep Learning, Machine Learning, Data Science, Amazon S3 (AWS S3), Amazon Athena, Jupyter Notebook, Data Analysis, Requirements Analysis

Data Scientist

2020 - 2020
MYRM Technologies, LLC
  • De-duplicated and cross-referenced customer records to be inserted from a disorganized collection of spreadsheets into the Salesforce system.
  • Designed a database used to migrate Salesforce data to a RoR based system.
  • Led import from various sources into the Salesforce system for efficient tracking of leads and progression to different stages of deal completion.
Technologies: Pandas, Salesforce, Matching Systems, Jupyter Notebook, Amazon Athena, Data Analysis

Lead Data Scientist

2017 - 2020
  • Developed a hotel-ranking model that used a user's recent interactions to show relevant results.
  • Built a user intent prediction model based on a customer's activity in the eCommerce funnel.
  • Constructed the NLP part of a chatbot for handling the post-sales requirements of the business.
  • Collaborated on the design of a feature marketplace—a kind of data warehouse that combined data from several sources for use by data science models.
  • Created a universal search for the travel domain which allowed users to search for hotels and flights using free text. This involved the application of NLP techniques to extract relevant fields from the text.
Technologies: Amazon SageMaker, PyTorch, Amazon Web Services (AWS), PySpark, Data Science, NumPy, Pandas, Apache Airflow, Redshift, Spark, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Machine Learning, Python, Artificial Intelligence (AI), Algorithms, Data Analysis, Amazon S3 (AWS S3), NoSQL, Amazon Athena, Jupyter Notebook, Microsoft Power BI

Data Scientist | Analyst

2019 - 2019
Mix Tech (via Toptal)
  • Set up various dashboards over Redshift and Metabase to understand how the product was performing among different customer segments and devices.
  • Analyzed customer data and monitor stats like user retention, app installation/uninstallation rates, user engagement, daily/weekly/monthly/quarterly performance, and customer movement through the funnel, etc.
  • Developed a churn model using PySpark and Python which was used to target customers based on their probability of churn.
Technologies: Amazon Web Services (AWS), Data Analytics, Spark, Machine Learning, Metabase, SQL, Redshift, Python, Data Analysis, Data Modeling, Amazon S3 (AWS S3), Amazon Athena, Jupyter Notebook, Microsoft Power BI

Research Assistant

2015 - 2017
Universitat Pompeu Fabra
  • Developed a model linking household wealth to female infanticide in India through the marriage market.
  • Estimated the structural model and conducted counterfactual policy simulations to inform interventions. Implementation using Amazon Web Services (AWS) for the heavy computational tasks.
  • Developed theoretical solutions of the model with derivation of the equilibrium equations and checking the proofs. Simulated the model economy in Matlab.
Technologies: Mathematica, MATLAB, Python, Economics, Data Modeling

Feature Marketplace for Data Science

I developed a feature store in AWS Redshift that collates data from a number of different sources and makes them available in the desired format. It made the data clean, was always up-to-date, and ready to be used by machine learning models in production.

Data Tagging Tool

I improved an open-source data labeling tool in Django to create training data for an NLP classifier which was used in a chatbot. It enabled support for the dynamic options for every instance to be labeled.


I developed a machine learning model to show personalized ranking to users based on their historical and recent interaction with products as well as similarity with other users.

South India Community Study

I worked on research projects on the economics of social networks in South India involving a randomized control trial.
I developed and customized a name-matching algorithm to match incoming patients to the project’s census data.

Predict 'em All

I developed an R-shiny-based machine learning application that predicts which Pokemon creature you would encounter at a given location and time in the Pokemon GO mobile game. The ML model was trained on a large publicly available dataset of the game.

Real-time Multiplayer Game

I developed a real-time multiplayer game integrating Microsoft Kinect and Windows Phone that allows one player using the phone to generate obstacles for the player using the Kinect.

Chatbot Intent Classifier

I made a deep learning based intent classification model for the chatbot of MakeMyTrip, the largest OTA in India. This intent classifier was based on the ULMFiT model. It is able to classify an intent among over a 100 classes.

Slot Extraction and Intent Classification

I developed a joint model based on sequence to sequence (Seq2Seq) architecture which allows a user to extract the intent and slot values from an utterance given to a chatbot.

Medical Document Understanding

A Python-based app for classifying and parsing medical documents (including lab reports, prescriptions, vaccination certificates, etc.).

This makes the documents digitally available as well as searchable. This is very similar to what Google Photos does for unorganized photos. It makes all your medical documents organized in proper categories and easily searchable with the relevant medical terms, even if they are handwritten.
2016 - 2017

Master's Degree in Data Science

Barcelona Graduate School of Economics - Barcelona, Spain

2011 - 2015

Bachelor's Degree in Economics

Indian Institute of Technology Kanpur - Kanpur, India


Pandas, PySpark, NumPy, SpaCy, PyTorch, TensorFlow


Git, Jupyter, Redash, Apache Airflow, Amazon Elastic MapReduce (EMR), Amazon SageMaker, Amazon Athena, Microsoft Power BI, Amazon QuickSight, MATLAB, STATA, LaTeX, PyCharm, Mathematica


Spark, Django, Seq2Seq


Python, SQL, R, C, Java, Scala


Data Science, Requirements Analysis


Linux, MacOS, Amazon Web Services (AWS), Jupyter Notebook, Docker, Unix, Salesforce


MySQL, Redshift, Apache Hive, Amazon S3 (AWS S3), Data Pipelines, Elasticsearch, NoSQL


Deep Learning, Statistics, Predictive Learning, Predictive Modeling, Data Visualization, Data Engineering, Analytics, Big Data, Economics, Machine Learning, Natural Language Processing (NLP), Data Analytics, Artificial Intelligence (AI), Algorithms, Data Analysis, Machine Learning Operations (MLOps), Generative Pre-trained Transformers (GPT), Data Matching, Statistical Modeling, Inventory Management Systems, Recommendation Systems, Data Modeling, Metabase, Custom Audio Embedding, Computer Vision, Matching Systems

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