Joy Chatterjee

DCMN

Specialize in developing end-to-end ML infrastructures and solutions to tackle complex marketing challenges.

Work with and integrate advanced platforms such as Codeium, Cursor AI, ChatGPT, replicate.ai, fal.ai and many more to streamline research and accelerate development. Off and on designing scalable pipelines on native and cloud platforms, to activate real-time predictive models, time series forecasting, customer segmentation, ROI optimization and more.

Lead AI Engineer

- Led the project for multivariate time series forecasting of client campaigns, resulting in significant insights in ROI for TV campaigns.
- Implemented LLM-based chatbots for internal teams, using RAG architectures and context
tuning models.

Senior Data Scientist

The Joy of Data

Product Ops & Data Consultant, with over 10 years of experience in data science and three years in product led growth (PLG). Founder of The Joy of Data, based in Berlin.

For data, collaborative ventures with Mercedes, FedEx, Just Mad as well as AI startups such as brighter AI, DCMN focussing primarily into leveraging data for strategic product led growth.

For product, over the past three years, been a data partner for product teams. Responsible for boosting Product Led Growth (PLG) strategies through the application of data science methodologies and data analytics. Expertise domain includes proficiency in product operations, OKRs, KPI driver trees, cohort analysis, user segmentation and much more.

Founder

ShowHeroes Group

Data Scientist (In House Consulting)

brighter AI

Data Scientist

Data Engineer

Mercedes-Benz Research & Development North America, Inc.

Mavrx Inc.

Working on specific project oriented machine vision tasks to aid the system of smart farming techniques developed and monitored by Mavrx Inc.

Machine Learning Engineer

MARKABLE

Rochester Institute of Technology

Work at the lab mainly revolves around projects in the genre of algorithm development. The algorithms are aimed at solving various problems from the image processing and computer vision communities.

Graduate Research Assistant

Peer Advisor Leader

CESC Ltd.

Worked with a team of four to establish and implement control algorithms to modulate the outlet steam efficiency of the central boiler system in a thermal power plant.

Technical Intern

🌱 Founder, The Joy of Data 🎯 Helping Product Teams Grow With Data Science 🚀 Ex-Mercedes | Lead Data Scientist 🎙️ Speaker, Podcasts, YouTube

🚀 Lead AI Research Engineer | Data Science Expert | Product Coach 🚀

With over 11 years in Data Science and AI, I specialize in developing innovative, Data-Driven and AI solutions that elevate Product performance and drive Business Growth. I bring a unique blend of hands-on technical skills and strategic Product insight, supporting teams to leverage Data for informed decision-making and robust growth strategies.

🔹 Key Highlights:
	•	LLMs & Chatbots: Implemented advanced LLM-based chatbots with RAG architectures, boosting team efficiency and workflow.
	•	Predictive Analytics: Achieved a significant ROI increase on client TV campaigns through multivariate time series forecasting.
	•	MLOps & Automation: Created scalable pipelines with Docker & Kubernetes, ensuring seamless deployment and monitoring.
	•	Patented Innovations: Primary author on two patented ML technologies, focusing on object tracking and anonymization.
	•	Cross-functional Leadership: Successfully lead teams and mentor junior Data Scientists, ensuring collaborative, data-driven decision-making.

🔹 Technical Arsenal:
Python 🐍 | SQL 🗃️ | Big Data Ecosystems | Statistical Modelling 📊 | A/B Testing | Cloud Environments (AWS, Azure)

🎙️ Speaking Engagements :
An avid speaker at prestigious Data Science and AI conferences over the past two years, where he shares his insights and experiences to foster a collaborative and informed Data Science community.

Lead Data Scientist

Philips

R&D for Endovascular Robotic Procedure Automation

Worked on building the next generation ICE Catheter guidance platform with applications to Structural Heart Diseases.

Experience researching novel AI based image guided therapy features for peripheral atherectomy devices

Scientist

Elucid

Research Scientist - Image Processing

PhD Dissertation: Segmentation of CT image of cochlea using classical computer vision and deep learning techniques to facilitate drug flow modeling within cochlear compartments
* Using Capsule Networks over regular CNNs to improve performance and reduce model size
* Network design and optimization for 3D volumetric segmentation of medical images.
* Classical computer vision approach of atlas based registration and segmentation for pre-processing the data on Insight Toolkit on C++ platform.
* Extract concentration profiles of contrast agent within the ear and model diffusion parameters using inverse optimization methods.

Worked on 2 projects
1. New eye tracker method based on methods other than P-CR algorithm (Patent Pending): Worked on parallelizing computationally expensive parts of the algorithm on a GPU. Used OpenCV with CUDA module on C++ platform.

2. Effects of Vision on Biomechanics: Study of how an individual's visual system responds to obstacles in their way. Parameters varied being obstacle height, obstacle distance from start, real vs. virtual objects. Contribution involved data collection and analysis of previous collected data. Experiments were conducted in an Augmented Reality setup for displaying the virtual object and the subjects walked in a controlled AR environment.

Graduate Teaching Assistant

* Team member: Project on new two sensor scanner technology.
* Work was part of grant received by Hewlett-Packard.
* Applied and obtained patent on the new technology (Patent Pending: US20180288281A1)

Using TEE and X-ray with a combination of deep learning and computer vision to create smart solutions to aid in non-invasive cardiac procedures.

Artificial Intelligence Intern

University of Rochester

* Spearheaded the tendon 3D ultrasound image segmentation project.
* Optimized and designed networks to work with the small limited dataset.

Data Scientist Intern

Los Alamos National Laboratory

* Took responsibility of starting the cancer pathology slide classification project.
* Used core imaging concepts to pre-process the data.
* Established baselines using sparse coding techniques and deep CNN networks (VGG, ResNet).
* Work was presented at CAFCW 2017, and eventually published in BMC Bioinformatics Journal.

Applied Machine Learning Research Intern

Kodak Alaris

Part time work to analyze the need of imaging products to customers and come up with a process to introduce a new product.

taplio

The Battle

Joy Chatterjee

Sanketh S Moudgalya

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