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

Data Analytics Developer in Bengaluru, Karnataka, India

Member since June 3, 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.
Akhil is now available for hire




Bengaluru, Karnataka, India



Preferred Environment

Unix, Jupyter, PyCharm, Git, Python

The most amazing...

...project I've worked on was to predict a user's probability of purchase based on activities in an eCommerce funnel.


  • Data Scientist

    2017 - PRESENT
    • 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: Python, Machine Learning, NLP, Spark, Redshift, Airflow, Pandas, NumPy
  • 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: Python, Redshift, SQL, Metabase, Machine Learning, Spark
  • 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.
    Technologies: Python, MATLAB, Mathematica


  • Feature Marketplace for Data Science (Development)

    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 (Development)

    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.

  • Ranking (Development)

    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 (Development)

    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 (Development)

    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 (Development)

    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 (Development)

    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 (Development)

    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.


  • Languages

    Python, SQL, R, C, Java, Scala
  • Frameworks

    Spark, AWS EMR, Django
  • Libraries/APIs

    Pandas, PySpark, NumPy, SpaCy, PyTorch
  • Tools

    Git, AWS Athena, Jupyter, Redash, Apache Airflow, Amazon SageMaker, MATLAB, STATA, LaTeX
  • Paradigms

    Data Science
  • Platforms

    Linux, MacOS, Amazon Web Services (AWS), Docker
  • Storage

    MySQL, Redshift, Apache Hive, Elasticsearch
  • Other

    Machine Learning, Data Analytics, Natural Language Processing (NLP), Statistical Modeling, seq2seq, Computer Vision


  • Master's degree in Data Science
    2016 - 2017
    Barcelona Graduate School of Economics - Barcelona, Spain
  • Bachelor's degree in Economics
    2011 - 2015
    Indian Institute of Technology Kanpur - Kanpur, India

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