Rishab is available for hire
Hire RishabRishab Pal
Verified Expert in Engineering
Data Scientist and AI Developer
Location
Bengaluru, Karnataka, India
Toptal Member Since
November 10, 2022
Rishab is an experienced data scientist and machine learning expert with a strong passion for mathematics and AI. He has over five years of experience designing and developing end-to-end machine learning solutions, which have been successfully deployed to production on cloud platforms. Rishab is constantly investing in his skills to stay up-to-date with the latest advancements in the field and is committed to applying his knowledge and expertise to develop impactful solutions.
Portfolio
Nolea Technology Ltd
Python, Scikit-learn, Pandas, Amazon Web Services (AWS)...
Revibe AI
GPT, Python, Chatbot Conversation Design, API Integration...
Zachary Gidwitz
Data Science, Machine Learning, Python, Recommendation Systems, Data Strategy...
Experience
Availability
Part-time
Preferred Environment
Machine Learning, Deep Learning, Data Science, Hugging Face, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Computer Vision, Data Scientist, Python, ChatGPT
The most amazing...
...accomplishment I've had was showcasing my project at CES—International Consumer Electronics Show, the technology sector's flagship trade fair, in 2019.
Work Experience
AI/ML Expert
2023 - 2024
Nolea Technology Ltd
- Built an MVP for the recommendation system the client wants to use for their platform, where they match the healthcare companies with clinical specialists across mental healthcare, allied healthcare, orthopedic specialists, etc.
- Revamped their recommendation engine using SOTA NLP approaches and machine learning models like NER, LayoutLM, LayoutParser, and phrase/doc embeddings.
- Used a cloud-native architecture previously built on a rules-based engine that lacked robustness for their business model.
Technologies: Python, Scikit-learn, Pandas, Amazon Web Services (AWS), Artificial Intelligence (AI), Machine Learning, Recommendation Systems
LLM Chatbot Developer
2023 - 2023
Revibe AI
- Completed a POC of a mental health assistant built on top of Voiceflow.
- Worked on the chatbot replicates the behavior of a neuroscientist and is emphatic to the user during the conversation.
- Collaborated in creating the chatbot that understands the user's state of mind, responds with appropriate suggestions/steps, or engages in a general conversation as necessary for the given knowledge base.
Technologies: GPT, Python, Chatbot Conversation Design, API Integration, Natural Language Understanding (NLU), Natural Language Processing (NLP), Language Models, Artificial Intelligence (AI), Voiceflow, Cognitive Behavioral Therapy (CBT), Zapier, Machine Learning, Chatbot
Machine Learning Expert / Data Scientist
2023 - 2023
Zachary Gidwitz
- Built a recommendation engine for an addiction recovery app that incorporates user demographics, mental state, and other metadata from app interaction to suggest quotes and prayers relevant to the user's state of mind.
- Personalized user interaction via ChatGPT using user metadata. Provided several offline and online metrics to track the live performance of the model and the recommendation system.
- Oversaw the launch process, resulting in over 50,000 downloads in the 1st month.
Technologies: Data Science, Machine Learning, Python, Recommendation Systems, Data Strategy, Data, Chatbots, OpenAI GPT-3 API, Jupyter, Cloud, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Airtable, Machine Learning Automation, Project Management, Statistical Analysis, Data Reporting, Data Cleansing, Prompt Engineering
Machine Learning Engineer
2023 - 2023
Odem Global Pty Ltd
- Used Bittensor's Mountain training data to train a model better than the client already had. Bittensor is a peer-to-peer intelligence market and an open-source protocol powering a scalable, globally-distributed, decentralized neural network.
- Improved the performance on the validation set through the above-mentioned task, thereby increasing the reward.
- Contributed to model hyperparameter tuning. Improved the performance of the default DeepSpeed PyTorch data loader for faster dataset loading and training.
- Evaluated the model's performance on the validation set to ensure it meets the desired performance metrics.
Technologies: Language Models, Machine Learning, Fine-tuning, Causal Inference, DeepSpeed, OpenAI GPT-3 API, Jupyter, Cloud, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Large Language Models (LLMs), Project Management, Regex, Statistical Analysis, Data Reporting, Data Cleansing, Prompt Engineering, Chatbot
AI Expert
2023 - 2023
Rich Lemon Apps FZE LLC
- Developed a user sticker model using Stable Diffusion and ControlNet for a given style.
- Experimented with different configurations of parameters to understand the effect on the results and achieved state-of-the-art results.
- Explored different methods like checkpoint merging, trained a stickers model, and then fine-tuned that model on Portrait Face, and combined sticker style and user image training.
Technologies: Artificial Intelligence (AI), Deep Learning, Image Processing, Stable Diffusion, Computer Vision, ControlNet, 3D, Microservices Architecture, Data Scientist, Jupyter, Cloud, Integration, Programming, AI Programming, Data Cleaning, Machine Learning Automation, Image Analysis, Project Management, Statistical Analysis, Data Reporting, Data Cleansing, Prompt Engineering, Image Generation, Text to Image
Senior Machine Learning Expert / Data Scientist
2021 - 2023
EagleView
- Developed VirtualHR, a tool to automate job descriptions matching with resumes to get the top candidates out of thousands using fastText skip-gram embeddings, named-entity recognition, LayoutLM, Layout Parser, and tree-based search algorithms.
- Built SOTA computer vision techniques for US housing insurance domain problems. Used aerial imagery for eave height calculation and improved median root mean squared error (RMSE) from 30% to 6% resulting in a 70% reduction in potential penalties.
- Revamped a measurement automation solution for US residential roofs by training a new line detection model. Fine-tuned the model for best results and designed intelligent heuristics-based post-processing to refine model outputs.
- Hacked multiple complex PyTorch models to enable their open neural network exchange (ONNX) and TensorRT inference for efficient deployment.
Technologies: Python, Machine Learning Operations (MLOps), PyTorch, Python API, Docker, Computer Vision, GPU Computing, GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Data Science, SQL, Neural Networks, Data Visualization, Unsupervised Learning, Code Review, Remote Team Leadership, Graphics Processing Unit (GPU), Transfer Learning, Transformers, Sequence Models, Image Retrieval, Image Recognition, Classification, Entity Extraction, Siamese Neural Networks, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), TensorBoard, Generative Adversarial Networks (GANs), Linux, Language Models, Python 3, Recommendation Systems, Algorithms, XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Amazon Web Services (AWS), Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, RAPIDS, APIs, Amazon SageMaker, NoSQL, Image Search, AI Design, Jupyter Notebook, Flask, NVIDIA TensorRT, Microservices Architecture, Data Scientist, Jupyter, Cloud, Anomaly Detection, Integration, Programming, AI Programming, Bitbucket, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Kubernetes, Amazon Elastic Container Service (Amazon ECS), Image Analysis, Large Language Models (LLMs), Project Management, Statistical Analysis, Data Reporting, Data Cleansing, You Only Look Once (YOLO)
Senior Software Engineer/Machine Learning (ML)
2020 - 2021
Airtel India
- Designed and built a chatbot, leveraging HuggingFace transformers with DeepSpeed to resolve queries for over 400 million of Airtel's monthly active users. Deployed successfully, handling over 10 million daily conversations with 85% accuracy.
- Improved the accuracy of face recognition, matching, and liveness-check models. Added face segmentation, blur and forgery detection, real-time blacklisting, and face search. Enhanced service efficiency and reduced manual effort by over 25%.
- Inculcated best MLOps practices on the team to lower technical debt and adapt workflow that requires little to no manual intervention for simulations, testing, deployment, and monitoring.
Technologies: Data Analytics, Statistical Modeling, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), GPT, Python 3, Docker, Bash Script, Data Science, SQL, OCR, Neural Networks, Optimization, Clustering, Code Review, Remote Team Leadership, Graphics Processing Unit (GPU), Transfer Learning, Transformers, Sequence Models, Image Retrieval, Image Recognition, Classification, Entity Extraction, Recurrent Neural Networks (RNNs), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), TensorBoard, Generative Adversarial Networks (GANs), Topic Modeling, Linux, Language Models, PostgreSQL, Recommendation Systems, Algorithms, Generative Pre-trained Transformer 3 (GPT-3), XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Amazon Web Services (AWS), Handwriting Recognition, Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, RAPIDS, APIs, Amazon SageMaker, NoSQL, Image Search, Amazon Rekognition, AI Design, Jupyter Notebook, Flask, NVIDIA TensorRT, Microservices Architecture, Data Scientist, Chatbots, Jupyter, Cloud, Anomaly Detection, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Bitbucket, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Kubernetes, Amazon Elastic Container Service (Amazon ECS), Image Analysis, Large Language Models (LLMs), Project Management, Statistical Analysis, Data Reporting, Data Cleansing, Big Data, Chatbot
Machine Learning Engineer
2019 - 2020
Trantor Software
- Designed and deployed an OCR solution that outperformed the accuracy of Google OCR by 12% as benchmarked on the ICDAR 2013 dataset. Reduced the overall cost from an estimated $1 million to less than $50 thousand per year.
- Prototyped a natural language processing (NLP)-based bot using recurrent neural networks to parse comments on the website, assess sentiments, and classify them into themes. Enabled standard responses and activation of the needed systems to respond.
- Worked on a trained convolutional neural networks model using INT8 fixed-point arithmetic. Designed algorithms to decide the Q. Collaborated to enable classifiers, detectors, Generative Adversarial Networks (GANs), and segmentation networks.
- Contributed to software categorization and brand recognition based on the product title or description using Word2Vec, n-grams, embeddings, and k-nearest neighbors classifiers.
- Explored casual language modeling using OpenAI GPT, fine-tuning the language model on a custom dataset for customer service and documentation.
- Managed quantization-aware training of convolutional neural networks and worked on fine-tuning and post-training quantization.
Technologies: Machine Learning, Deep Learning, Data Science, SQL, Artificial Intelligence (AI), OCR, Data Visualization, Optimization, Unsupervised Learning, Clustering, Code Review, Remote Team Leadership, Graphics Processing Unit (GPU), Transfer Learning, Transformers, Sequence Models, Image Recognition, Classification, Recurrent Neural Networks (RNNs), Siamese Neural Networks, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), TensorBoard, Generative Adversarial Networks (GANs), Topic Modeling, Linux, Language Models, Python 3, Recommendation Systems, Algorithms, Generative Pre-trained Transformer 3 (GPT-3), XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Handwriting Recognition, Google Vision API, Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, RAPIDS, APIs, Amazon SageMaker, NoSQL, Image Search, Amazon Rekognition, AI Design, Jupyter Notebook, Flask, Microservices Architecture, Data Scientist, Chatbots, Jupyter, Cloud, Google Cloud Platform (GCP), Anomaly Detection, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Image Analysis, Large Language Models (LLMs), Project Management, Regex, Statistical Analysis, Data Reporting, Data Cleansing, You Only Look Once (YOLO), Chatbot, Elasticsearch
Software Engineer
2018 - 2019
Yamaha Motor Solutions
- Conceptualized and built a ConvLSTM model on TensorFlow for hand gesture recognition. Used Bayesian optimization techniques for hyperparameter tuning.
- Worked on single-shot face recognition using SSD MobileNet for detection and Inception-ResNet v2 for feature extraction. Introduced TP-GAN for photorealism and identity, preserving the frontal face-view synthesis from any pose.
- Developed predictive analytics solutions for retail sales forecasting using ARIMA, exponential smoothing, and Holt-Winter for time series analysis.
- Implemented conditional Wasserstein generative adversarial network (cWGANs) to synthesize new vehicles design from given samples.
Technologies: Python 3, Machine Learning, Data Science, SQL, Artificial Intelligence (AI), Neural Networks, Data Visualization, Optimization, Code Review, Graphics Processing Unit (GPU), Transfer Learning, Machine Vision, Sequence Models, Classification, Siamese Neural Networks, Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), TensorBoard, Generative Adversarial Networks (GANs), Linux, Algorithms, XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Handwriting Recognition, Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, APIs, Amazon SageMaker, NoSQL, AI Design, Jupyter Notebook, Flask, Data Scientist, Jupyter, Cloud, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Image Analysis, Project Management, Regex, Statistical Analysis, Data Reporting, Data Cleansing, You Only Look Once (YOLO), Point Cloud Data, Point Clouds
Experience
Artificial Intelligence-based Document Capturing and Processing | AIDCAP
https://randomtrees.com/dcap1. I fully automated the document capture and information extraction process using NLP and CV and it mainly dealt with invoices and orders from different sources, including emails, PDFs, DOCX, or images.
2. Built and trained custom models for region proposal and text recognition with enhanced semantic understanding using Transformer.
2. Built and trained custom models for region proposal and text recognition with enhanced semantic understanding using Transformer.
Search Relevance and Ranking
https://www.airtelxstream.in/1. Improved Search Relevance and Ranking metrics like click-through rates (CTR), conversion rate, and ads revenue for organic and ads merchants at an eCommerce site.
2. I alternated search keyword generation, increasing ad revenue by 20%. The system is scaled to handle >2,000 RPS using a Milvus-based vector search and is automatically refreshed daily. I then worked on a semantic search.
3. A custom deep semantic matching model trained for matching search queries with products. This resulted in a 10% growth in ad revenue. I also worked on search query classification using custom-trained embeddings for query understanding to match the search query with product leaf categories.
4. This project improved CTR by 50%, leading to a 37% incremental uplift in revenue and a 17% decrease in discount utilization. I improved the CTR using machine learning (ML)-based learn-to-rank models starting from simple linear regression and tree-based models, such as LightGBM and XGboost. These were combined with deep models for search-query understanding.
2. I alternated search keyword generation, increasing ad revenue by 20%. The system is scaled to handle >2,000 RPS using a Milvus-based vector search and is automatically refreshed daily. I then worked on a semantic search.
3. A custom deep semantic matching model trained for matching search queries with products. This resulted in a 10% growth in ad revenue. I also worked on search query classification using custom-trained embeddings for query understanding to match the search query with product leaf categories.
4. This project improved CTR by 50%, leading to a 37% incremental uplift in revenue and a 17% decrease in discount utilization. I improved the CTR using machine learning (ML)-based learn-to-rank models starting from simple linear regression and tree-based models, such as LightGBM and XGboost. These were combined with deep models for search-query understanding.
Callup AI | Chatbot Platform
https://www.airtel.in/contact-us1. A generative chatbot for user queries using a Transformer-based Seq2Seq model from chat messages. It is currently deployed commercially, handling thousands of daily conversations in English or Hinglish with almost 85% of accuracy.
2. It also comprised the nearest neighborhood-based intent detection solution using pre-tagged customer messages for tagging new customer messages to the support team.
3. The idea was to reduce customer service workload by automatically replying to user messages with pre-defined responses based on intent detection.
2. It also comprised the nearest neighborhood-based intent detection solution using pre-tagged customer messages for tagging new customer messages to the support team.
3. The idea was to reduce customer service workload by automatically replying to user messages with pre-defined responses based on intent detection.
VirtualHR | Candidate Recommendation Engine
https://www.facebook.com/ViRu-AI-110777420700733/1. A tool to automate matching job descriptions with resumes, facilitating the process of getting top candidates out of thousands based on several parameters, namely experience, education, skillset, projects, location, etc., and providing a weighted similarity score based on input parameters.
2. I automated the end-to-end process, leveraging the state-of-the-art (SOTA) NLP and machine learning approaches with cloud deployment.
2. I automated the end-to-end process, leveraging the state-of-the-art (SOTA) NLP and machine learning approaches with cloud deployment.
Retail Sale Forecasting
https://www.yamaha-motor-india.com/1. Analyzed the sales trends and sizing curve distribution across categories and product lines to give recommendations on the localized sizing distribution for a demand forecast model.
2. I proposed and implemented a predictive analysis for biweekly retail sales forecasting using ARIMA, exponential smoothing, and Holt-Winter methods.
3. The next step was incorporating Bayesian hyperparameter tuning with hybrid modeling using principal component analysis, support vector machines, and ensemble learning methods with Random Forests and XGBoost. This led to a 10% year-on-year increase in key metrics such as sales revenue.
2. I proposed and implemented a predictive analysis for biweekly retail sales forecasting using ARIMA, exponential smoothing, and Holt-Winter methods.
3. The next step was incorporating Bayesian hyperparameter tuning with hybrid modeling using principal component analysis, support vector machines, and ensemble learning methods with Random Forests and XGBoost. This led to a 10% year-on-year increase in key metrics such as sales revenue.
Churn Prediction
1. Initiated customer churn prediction in a subscription-based purchase model. The first use case was for a telecommunication provider, and the second was for a retail giant.
2. I leveraged, structured, and unstructured sales and customer information. This included chat, ratings, reviews, email, and voice chat transcripts for designing KPIs. The model could predict churn almost two weeks before it happened.
3. The next step was ideating and implementing an A/B test framework and SQL queries. The framework defined key metrics to analyze model performance statistical significance.
2. I leveraged, structured, and unstructured sales and customer information. This included chat, ratings, reviews, email, and voice chat transcripts for designing KPIs. The model could predict churn almost two weeks before it happened.
3. The next step was ideating and implementing an A/B test framework and SQL queries. The framework defined key metrics to analyze model performance statistical significance.
Virtual Try-on
https://vimeo.com/4765180181. A real-time apparel-based virtual try-on using a cutting-edge complex ML pipeline. It provides a real-time, immersive, clothing-trial experience on a phone or kiosk with a single image and well-thought-out recommendations that will eventually help in more sales.
2. Increased in-store purchases by over 25% and via-app purchases by over 20%.
2. Increased in-store purchases by over 25% and via-app purchases by over 20%.
Face Dedupe Platform | Face Recognition, Matching, and Retrieval
https://www.airtel.in/bank/video-kyc1. A platform with various microservices, including face recognition, face matching, and face liveness-check models.
2. I updated the platform with face segmentation, face blur detection, document forgery detection, real-time blacklisting, and face search to improve service efficiency and reduce manual effort by over 25%.
2. I updated the platform with face segmentation, face blur detection, document forgery detection, real-time blacklisting, and face search to improve service efficiency and reduce manual effort by over 25%.
Price Optimization
https://tiki.vn/1. Worked on optimization of prices of retail products for Tiki using demand prediction by ML models. I used time-series historical sales data; a LightGBM model is trained to predict sales at daily and three-day frequencies.
2. The work included data analysis, feature engineering, stakeholder management, designing A/B experiments, and performing statistical analysis for hypothesis testing.
2. The work included data analysis, feature engineering, stakeholder management, designing A/B experiments, and performing statistical analysis for hypothesis testing.
Skills
Languages
Python, SQL, Python 3, Regex, Bash, Snowflake, JavaScript, Bash Script
Frameworks
Flask, Accelerate
Libraries/APIs
TensorFlow, PyTorch, Python API, OpenCV, Scikit-learn, Pandas, NumPy, SciPy, Natural Language Toolkit (NLTK), SpaCy, XGBoost, RAPIDS, REST APIs, Google Vision API, Keras, Amazon Rekognition, DeepSpeech
Tools
TensorBoard, Amazon SageMaker, Jupyter, Bitbucket, You Only Look Once (YOLO), OpenAI Gym, Amazon Elastic Container Service (Amazon ECS), Dialogflow, NGINX, GIS, CPLEX, Apache Airflow, AutoCAD, Zapier
Paradigms
Data Science, Siamese Neural Networks, ETL, Microservices Architecture, Automation, Anomaly Detection, Microservices
Platforms
Linux, Jupyter Notebook, Docker, Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), Kubernetes, Mixpanel, Blockchain, Voiceflow
Industry Expertise
Project Management
Other
Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Software Deployment, Software Design, Statistics, Statistical Modeling, Data Analytics, Machine Learning Operations (MLOps), Image Processing, BERT, Long Short-term Memory (LSTM), Predictive Modeling, Hugging Face, Artificial Intelligence (AI), OCR, Neural Networks, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Time Series Analysis, Entity Extraction, Classification, Image Recognition, Image Retrieval, Object Detection, Sequence Models, Machine Vision, Transformers, Transfer Learning, Graphics Processing Unit (GPU), Remote Team Leadership, Code Review, Generative Adversarial Networks (GANs), Clustering, Topic Modeling, Data Visualization, Mathematics, Optimization, Language Models, Recommendation Systems, Algorithms, Data Collection, Data Extraction, Data Manipulation, Large Data Sets, Analytics, Data Analysis, Models, Version Control Systems, Communication, Modeling, Data Engineering, Regression, APIs, Image Search, AI Design, Fine-tuning, Causal Inference, Data Scientist, GPT, Generative Pre-trained Transformers (GPT), Decision Trees, Chatbots, Cloud, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Language Learning, Image Analysis, Large Language Models (LLMs), Statistical Analysis, Data Reporting, Data Cleansing, API Integration, Prompt Engineering, Image Generation, Text to Image, Chatbot, Distributed Systems, Multivariate Statistical Modeling, GPU Computing, Big Data, Web Scraping, Unsupervised Learning, Generative Pre-trained Transformer 3 (GPT-3), Revenue Optimization, Pricing Strategy, Handwriting Recognition, DeepSpeed, Stable Diffusion, NVIDIA TensorRT, Software Architecture, ChatGPT, OpenAI GPT-3 API, Financial Modeling, Cognitive Behavioral Therapy (CBT), Data Structures, User Journeys, Customer Journeys, Amazon API Gateway, Generative Artificial Intelligence (GenAI), Point Cloud Data, Application Packaging, Internet of Things (IoT), Industrial Internet of Things (IIoT), Rankings, Computational Biology, Oncology & Cancer Treatment, Genomics, Molecular Biology, Biology, A/B Testing, Bittensor, 3D, Diffusion Models, ControlNet, Data Strategy, Data, User Interface (UI), Airtable, Web Applications, Natural Language Understanding (NLU), Point Clouds
Storage
PostgreSQL, MySQL, NoSQL, Google Cloud, Elasticsearch
Education
2014 - 2018
Bachelor's Degree in Computer Science
DIT University - Dehradun, India
Certifications
SEPTEMBER 2019 - PRESENT
Deep Learning Specialization
Coursera
MAY 2018 - PRESENT
Machine Learning
Coursera
APRIL 2018 - PRESENT
Big Data Modeling and Management Systems
Coursera
MARCH 2018 - PRESENT
Programming the Internet of Things (IoT) Specialization
Coursera
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