Data Science Analytics

SHAUKAT SHAIKH

Location : Mumbai, Maharashtra Email : ******************@*****.*** Phone : 775-***-**** Github link : https://github.com/Shaukat-Shaikh Linkedin profile : https://linkedin.com/in/ShaukatShaikh71/ Summary

Experienced Data Science professional with hands-on expertise in developing ML/DL solutions using TensorFlow, PyTorch, and Scikit-learn. Proven track record in model optimization, data analytics, and solving business problems with statistical and predictive modeling. Actively working in the Generative AI space—building LLM-based applications, RAG pipelines, and deploying AI-driven systems for document understanding and automation. Passionate about turning data into actionable insights to drive business impact.

Professional Experience

Renew Instruments Wadala, Mumbai, Data science Engineer 1st April 2024 – 30th March 2025

• Developed advanced ML/DL models using Scikit-learn and TensorFlow, incorporating quantitative finance techniques to enhance predictive analytics and risk modeling.

• Worked with global equity factor models such as Barra and Axioma to analyze market trends and optimize investment strategies.

• Applied financial modeling expertise to equity options and futures, contributing to data-driven decision-making.

• Leveraged SQL and Python to process large financial datasets, optimize queries, and support quantitative research.

• Designed interactive dashboards using Power BI/Tableau to present insights to stakeholders and improve data visualization.

• Collaborated with cross-functional teams, including developers, analysts, and stakeholders, to streamline workflows and enhance model performance.

• Effectively managed multiple projects, meeting tight deadlines while ensuring high-quality deliverables.

• Utilized MS Office tools to document findings, create reports, and communicate financial insights effectively.

• Adapted to evolving market conditions and regulatory changes, resolving challenges efficiently in a fast-paced environment. Worko.ai Remote, AI Engineer Intern

Jun 2025 – Present

• Built an AI-driven resume-job matching system using LLMs and embedding-based similarity search.

• Designed and deployed RAG pipelines with LangChain, FAISS and ChromaDB for efficient document retrieval.

• Fine-tuned transformer models (e.g., MiniLM, sentence-transformers) on domain-specific data to enhance semantic matching and candidate scoring.

Technical Skills

Programming Language: Python. Database Language: SQL MLOPS: CICD, GitHub Action, DockerHub

Analytics: Numpy and Pandas Tools: Power BI, Advance Excel Cloud: AWS

AI: GenAI, LangChain, LangGraph, Data Pre-processing, Regression, Naive Bayes, Decision Trees, Support Vector Machines, K- Nearest Neighbors, Clustering, Principal Component Analysis, Boosting, Pytorch, Deep Learning (ANN, NLP, CNN)

Projects

Objective:

Built an interactive Streamlit-based web app that leverages MoonshotAI’s kimi-vl-a3b-thinking model to analyze medical report images and generate simplified health assessments. Tasks Performed:

• Integrated the MoonshotAI API to process base64-encoded medical report images and return model-generated summaries.

• Implemented secure API handling using environment variables and dotenv.

• Utilized PIL, requests, and tempfile for efficient image preprocessing and temporary storage.

• Applied regex to clean up and filter model responses for clarity and conciseness. link : https://heath-app.onrender.com

Project 2 : PORTFOLIO PRO – MASTERING STOCK SELECTION & PORTFOLIO OPTIMIZATION Objective:

Leverage Python and advanced data science techniques to identify high-potential stocks and construct an optimized investment portfolio using technical indicators and Modern Portfolio Theory (MPT). Tasks Performed:

Analyzed one year of historical data for 2,000 companies by evaluating key financial metrics (beta, standard deviation, skewness, kurtosis) to determine stock performance. Stock Selection with RSI:

Employed the Relative Strength Index (RSI) to pinpoint the best entry points. After filtering for promising stocks, the top 20 with the lowest RSI values were selected—indicating optimal buying opportunities. Portfolio Optimization:

Applied Modern Portfolio Theory (MPT) to calculate optimal asset weights, thereby maximizing the portfolio’s Sharpe Ratio while minimizing overall risk. This systematic approach yielded a notable 6.1% return over one month. link : https://github.com/Shaukat-Shaikh/Portfolio-Pro_Mastering_Stock_Selection PROFESSIONAL COURSES

IT VEDANT, THANE

MASTERS IN DATA SCIENCE WITH AI AND AWS 2022-2023

EDUCATION

MUMBAI UNIVERSITY

BSC (SMT.CHM COLLEGE ULHASNAGAR)

MUMBAI UNIVERSITY

2019-2022

HSC (ST.MERRY’S JR COLLEGE THANE) 2017-2018

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