Hi, I'mTejas Shinde
I solve real world data problems using Data Science & Applied Statistics
About Me
Senior Data Scientist with over 6 years of experience building and deploying end-to-end data science and machine learning solutions across Energy, Finance, Health and HR domains. Proven expertise in integrating statistical modelling, developing CI/CD pipelines, deploying advanced NLP frameworks, with business intelligence production ready dashboards. Awarded for delivering measurable business outcomes, including significant cost savings and operational efficiency. Passion to mentor teams and collaborating cross- functionally to create a safe space for innovation and playing with numbers together as a team.
My journey in tech started with a strong foundation in software development. I've worked with various companies to create intuitive, performant, and accessible digital experiences.
When I'm not coding, you can find me exploring new technologies, contributing to open-source projects, and staying up-to-date with the latest industry trends.
My Skills
Work Experience
Senior Data Scientist
I served in a dual role as a Data Scientist and Data Engineer, driving advanced analytics initiatives across supply chain optimization, Diverse Business Enterprise (DBE) programs, cost forecasting, and regulatory reporting. My work bridges engineering reliability with strategic insight—empowering business units to operate more efficiently through automation, machine learning, and cloud-native solutions.
Data Science Manager
At HSBC, I led impactful data science initiatives that automated decision-making, optimized infrastructure cost, and unlocked insights from complex datasets across HR, operations, and business analytics.
Senior Data Scientist
As a Senior Data Scientist at HSBC, I architected data science solutions that delivered measurable impact across HR, compensation, and enterprise analytics. My role centered around building scalable machine learning pipelines, automating insights delivery, and enabling data-driven workforce planning through a combination of ML, NLP, MLOps, and interactive visualization.
Featured Projects
Machine Learning
Neural Network Model as a part of Kaggle Contribution towards real clinical health data and creating a neural network from scratch
Auto Encoder
Used Auto Encoders [AE] to solve real world problems. The dimensionality reduction works on the basic working principal of AE when the state is reduced to latent space for representation we extract the latent space as it can represent the data efficiently . Generally we use PCA for it But AE perform better them PCA in almost all the given conditions
Deep Learning
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Natural Language Processing
Branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages. From simple NLTK and Spacy Models to complex RNNs and HuggingFace Frameworks, this includes all my experience in those frameworks
Clustering
Showcase clustring skills using Density Based and Hierarchical Based modelling to determine the intrinsic grouping among the unlabeled data present.
Reinforcement Learning
Using AI agents to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation.
Recommender Systems
Showcasing capability of predicting the future preference of a set of items for a user, and recommend the top items. Recommender systems are utilized in a variety of areas and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter.