Data Scientist Machine Learning
Resume:
Ritik Sharma
Data Scientist Pune, India
*************@*****.***
linkedin.com/in/ritik-sharma-358256172
Professional Summary
Results-driven Data Scientist with hands-on experience in machine learning, time series forecasting, MLOps, and scalable data engineering. Proven ability to deploy robust AI/ML solutions, automate S&OP workflows, and generate actionable insights that drive business performance. Core Competencies
• Programming: Python, SQL, PySpark, Bash
• ML & AI: Machine Learning, Deep Learning, Time Series Analysis, Statistics, LLMs
• MLOps & DevOps: MLflow, DVC, Docker, Kubernetes, GitHub Actions, CI/CD, Terraform
• LLMs: Transformers, RAG, LangChain, Agentic AI
• Data Visualization: Tableau (dashboards, storyboards), Looker
• Cloud Platforms: GCP, AWS, Azure, Databricks, Hadoop, Linux Professional Experience
S&OP Data Analyst, TE Connectivity, Pune, India Aug 2022 – Jan 2024
• Spearheaded data-driven decision-making for S&OP by analyzing cross-functional metrics across demand, supply, and sales.
• Reduced manual reporting time by 50% by automating KPI dashboards and ETL workflows using Python and SQL.
• Built scalable data pipelines using PySpark on Databricks and Dataflow to streamline data ingestion.
• Engineered deep learning models in PyTorch, improving SKU-level forecast accuracy by 20%.
• Implemented end-to-end deployment using MLflow, DVC, and Docker within CI/CD pipelines.
• Designed interactive Tableau dashboards for leadership, enabling dynamic tracking of demand/supply KPIs and reducing decision-making time by 30%.
Data Science Fellow, TE Connectivity, Pune, India Jan 2022 – Jul 2022
• Automated daily data workflows and embedded outputs into interactive dashboards for operational teams.
• Applied statistical and ML-based time series models to enhance demand forecasting accuracy. Highlighted Projects
Customer Liability Tracker: Developed a real-time anomaly detection and alerting system using PySpark, SQL, and Dataflow. Integrated with scikit-learn for modeling and deployed using Docker, MLflow, and CI/CD on AWS EC2. Achieved a 35% improvement in anomaly resolution time and reduced operational risk. Intelligent Forecasting System: Designed SKU-level demand prediction models using ARIMA, Prophet, and LSTM. Built scalable, distributed pipelines on GCP Dataproc with output stored on GCS, tracked via MLflow, and versioned using DVC. Enabled data-driven planning across 10+ product lines. LLM-Powered S&OP Advisor: Built a smart AI assistant for planners using Transformers, LangChain, RAG, and Agentic AI. Retrieved context from enterprise data sources and integrated ML-based forecasts. Deployed on multi-cloud with Docker and Kubernetes, reducing planning time by 40%. Predictive Analytics Platform for Business Insights: Engineered ML pipelines for classification/regression using XGBoost, scikit-learn, Random Forest, and RNN/LSTM. Tracked and deployed models via MLflow and Docker on GCP Cloud Run. Delivered real-time business KPIs through APIs and Looker and Tableau dashboards.
Education
M.Tech in Data Engineering 2024 – 2025
Indian Institute of Technology (IIT) Jodhpur, India B.Tech in Computer Engineering 2018 – 2022
Vishwakarma Institute of Technology, Pune, India
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