Data Analyst Machine Learning

SAI SURESH REDDY CHALLA

Boston, MA 857-***-**** ******.***@************.*** LinkedIn

EDUCATION

Northeastern University, Boston, MA Sep 2022 - May 2024

Master of Professional Studies in Analytics (Concentration: Applied Machine Intelligence) GPA: 3.86

WORK EXPERIENCE

Biocon Limited, Hyderabad, India. Apr 2022 - Aug 2022

Executive Project Analyst

Managed several Greenfield projects in the Electrical and Automation domains, ensuring project completion within time and budget constraints.

Conducted risk assessments, implemented safety measures, and ensured a safe working environment for employees

Analyzing project data using Excel and SQL, identifying areas for improvement, optimizing project efficiency, and achieving an 18% increase in revenue through collaboration with vendors and contractors for timely delivery of equipment and materials.

Increasing efficiency by developing automation systems such as SCADA and DCS.

Shanvr Life Sciences PVT LTD, Hyderabad, India. Nov 2020 - Mar 2022

Junior Data Analyst

Validated round trip efficiency for multiple projects by using Python, R, and SQL to conduct extensive research and analysis on energy storage devices and electrochemical batteries.

Automated data collection, organization, and pattern recognition in time-series data for economic optimization by creating machine-learning flow battery models in Python and employing R data analysis tools.

Evaluated physical models, experimental data, scholarly journals, and internal performance statistics to study electrochemical batteries and energy storage systems using Python, R, and MATLAB. I wrote in-depth reports on my research activities and conclusions.

Constructed a C++ flow battery design and operation optimization model using tools like TensorFlow and created a chemistry-neutral bottom-up analysis model in Python.

This led to decreased battery costs and widespread usage in the community by distributing them via social media.

Created R-based data analysis processes to automatically test and evaluate EV batteries for smart grid deployment.

I also predicted the daily production for a 1.2 MW solar installation using data analytics tools and approaches to maximize system reliability.

Extracting insights and identifying trends from vast amounts of data to support decision-making and propel business outcomes.

Kalven Enterprises, Hyderabad, India Mar. 2018 - Feb. 2020

Data Engineer Intern

Contributed to the development of automation tools to optimize account transactions and simulate consumer GST return procedures while working with the Financial Assessment department of the Singapore government.

Using DBT, Spark, and Azure, extensive machine learning pipelines were developed in cloud environments.

15% increase in decision-making efficiency using Python and MySQL to automate account transaction operations.

Created customer lifetime value and marketing mix models with Bayesian frameworks.

Used Bayesian techniques to carry out A/B testing to improve retail strategy.

Collaborating on ETL procedures with data engineers to guarantee excellent data quality and integrity.

CI/CD techniques were integrated with Azure DevOps to enable smooth model deployment.

TECHNICAL PROJECTS

Arbit Inc Sep 2023 - Dec 2023

Predictive Modeling & Dashboarding Analysis for the Sneaker Resale Market Python, Machine Learning

Utilized advanced data preprocessing and ML algorithms, including regression and clustering, on a 24.5 million rows Arbit Inc. dataset, generating actionable insights for sneaker resale.

Developed an interactive dashboard using matplotlib, seaborn, and predictive models like random forests and gradient boosting with 95% accuracy, enhancing decision-making and pricing strategies.

Contributed to a 30% profitability increase by leveraging ML techniques for pricing intelligence and data-driven strategies in the sneaker resale market.

H&M Fashion Recommendation Python, Recommendation Systems, Deep Learning Sep 2023 - Dec 2023

Implemented a recommendation system using clustering techniques to identify hidden patterns in sales data and understand customer and product sub-level groupings.

Engineered relevant features to capture local and global trends and deployed item-item collaborative, content-based, and deep learning-based models with a resulting MAP@12 of 0.0345.

Schneider Electric Apr 2023 – Jul 2023

Smart Alarm Management Exploration Python, NLP, Decision Trees

Reduced the workload of support staff and customers by efficiently classifying and managing more than 100 million Schneider Electric data center alerts using k-means clustering and decision trees.

Developed a reliable system to prioritize key warnings using supervised and unsupervised machine learning approaches, which led to a significant 35% reduction in incident resolution time for Schneider Electric's service teams and clients

Utilizing the enormous XN Project dataset, we used Python, Pandas, NumPy, Matplotlib, and Scikit-learn for data preprocessing, analysis, and modeling.

We achieved an outstanding 92% accuracy in alarm categorization, which improved operational effectiveness and allowed for more informed decision-making

Car Buying Trend Analysis R, Tableau, Regression Jan 2023 – Feb 2023

Utilizing statistical analysis techniques such as ANOVA, Tukey’s HSD test, and logistic regression to interpret and communicate statistical finding to non-technical stakeholders, essential for effective decision-making

Understanding of how age, income, and marital status can influence consumer behavior in automotive industry. Data-driven insights into customer preferences and purchasing behaviors.

Demonstrating proficiency in working with large datasets and drawing meaningful conclusions through expertise in statistical software tools such as R, essential for successful data analysis

Commercial Property Industry R, Regression, Machine Learning Sep 2022 - Oct 2022

Developed multiple regression models to forecast commercial property sales and leasing and to find the optimal model with high accuracy by cleansing and analyzing 0.9 million data samples

Projecting the Big Data Market to be worth $273.4 billion by 2026, as reported by Markets and Markets

Utilizing Big Data in the real estate sector to enable customers to find desired homes, select desired bedroom types, and identify appropriate neighborhoods in any region without the need for physical visits

TECHNICAL KNOWLEDGE

Languages : Python, Java, R, MySQL

Tools/ Technologies : Tableau, Snowflake, Databricks, Git, Airflow, Hadoop, A/B Testing, Discover, Power BI, Docker,

ETL, Vertex AI, Microsoft 365

ML Algorithms : Regression Algorithms, Decision Trees, Random Forest, SVM, Clustering, Recommendation

Systems

Frameworks and Libraries : Numpy, Pandas, Scikit-learn, Matplotlib, Keras, Scipy, Tensorflow, Pytorch, MLflow, PySpark

Certifications : AWS Certified Cloud Practitioner, Data Science BootCamp With R

SERVICE & LEADERSHIP

Part of the National Service Scheme in (2017 - 2020)

Organized marathons (Triathlon, Duathlon) and fests (technical and non-technical)

President of Power Audit Team (2017 - 2020) – GRIET

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