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Click here or scroll down to respond to this candidateCandidate's Name
DATA ANALYSTEmail: EMAIL AVAILABLE Phone: PHONE NUMBER AVAILABLE Location: Tempe AZ LinkedInPROFESSIONAL S UMMARY:Experienced Data Analyst with over 5 years of proven expertise in leveraging advanced analytics to drive business growth and strategic decision-making.Strong background in statistical analysis, machine learning, and data visualization; proven ability to produce insights that boost productivity and streamline operations.Adept with a variety of data manipulation tools, including Python, R, and SQL; committed to turning complicated datasets into compelling stories for stakeholders.Proficiency with various natural language processing (NLP) techniques, with a focus on production-level models that are scalable, for tasks including information extraction, sentiment analysis, and topic modelling.Extremely skilled in statistical modelling, machine learning, regression analysis, decision trees, random forests, SVM, Bayesian methods, XG Boost, and forecasting and predictive analytics. competent with Power BI, Excel, Tableau, and Adhoc Reporting.Proficient in AWS, Microsoft Azure, and GCP cloud computing.TECHNICAL SKILLS:Programming Languages:Python, R, SQL, Java, Scala, MATLABData Analysis:Exploratory Data Analysis (EDA), Data Cleaning, Pandas, NumPy, SciPy, Statistics, Data Visualization, Exploratory Data Analysis (EDA)Statistical Modelling:Regression Analysis, Hypothesis Testing, TensorFlow, Keras, PyTorch, XGBoost, Random ForestsMachine Learning:Supervised Learning, Unsupervised LearningData Visualization:Matplotlib, Seaborn, TableauTools Collaboration:GitHub, Jupiter Notebooks, Trello, SlackData Wrangling:Data Cleaning, Feature Engineering, Regular Expressions, JSON/XML ParsingDatabase:SQL Databases (e.g., MySQL, PostgreSQL), NoSQL Databases (e.g., MongoDB, Cassandra), Database Design and ManagementDeep Learning:ANN, CNN, RNN, LSTM, SOTA models, Attention and Transformers (BERT, GPT), Backpropagation, AllenNLP, Variational Autoencoder (VAE), Generative Adversarial Network (GAN), Diffusion architecture.Visualization Tools:Tableau, Power BI, Microsoft Excel.Packages:NumPy, Pandas, Matplotlib, OpenCV, Scikit-learn, Seaborn, TensorFlow, Spacy, PyTorch, NLTK, Pyspark.PROFESSIONAL EXPERIENCE:DATICS INC. Jan 2024 - PresentData EngineerUsing Apache Spark, scalable data pipelines were developed and maintained, processing over 10TB of raw data each month with 99.9% uptime.Gathered and preprocessed customer data from various sources, including transaction logs and demographic information, stored on AWS S3.Using ETL procedures to clean and transform data, 25% less data was inconsistent.Designed and implemented machine learning models with a team of five data engineers under my direction, increasing model accuracy by 15%.Employed Python and SQL to perform in-depth data analysis, finding trends and patterns to inform critical business choices.Created a machine learning (POC) model using PySpark for predictive analytics applications, transferring data from Azure to Google Cloud ML Engine.Using TensorFlow, advanced DNN and linear regression models were created, enabling precise package delivery time predictions in US residential and commercial regions.Created and extensively tested a hierarchical time series forecasting code that allows for the accurate forecasting of incoming package volumes at dispersed hubs for particular weeks in a production setting.Worked closely with a team of data engineers and BI analysts to improve the efficiency customer recommendation analytics engine by 33%.Automated processes for data validation that ensure data integrity across systems while saving 30% of manual labor.Increased client retention by 18% by using sophisticated statistical approaches to study consumer behaviour.Reduced equipment downtime by 25% by implementing predictive maintenance algorithms on data from IoT sensors.Utilized Docker and Kubernetes to orchestrate containerized applications, improving deployment efficiency by 50%.Mentored junior data scientists on best practices in data visualization, resulting in a 20% increase in dashboard usability.Trigent Software Sep 2020 Jul 2022Senior Data AnalystManaged projects from initiation to completion, overseeing all stages of the software development lifecycle (SDLC) in a sequential manner, ensuring successful delivery and client satisfaction.Applied DMAIC methodology to lead successful projects, resulting in $2 million in cost savings and a 60% reduction in defects.Played a key role in fostering a culture of continuous improvement by championing Six Sigma principles and methodologies across departments.Using statistical methods and machine learning algorithms, Analyzed and interpreted complicated datasets to provide business stakeholders with actionable insights.Lead the gathering, cleaning, and preprocessing of large datasets from diverse sources, ensuring data quality and suitability for analysis, achieving a data preparation accuracy rate of 95%.Led the integration of Google Analytics to track user interactions, extracting insights to enhance software products' customer experience.Developed and deployed sophisticated machine learning models, which improved customer churn analysis prediction accuracy by 25%Improved operational efficiency by 15% as a result of working with cross-functional teams to identify critical indicators and KPIs.To reduce data processing time by thirty percent, a lot of Python, R, and SQL were used for data extraction, transformation, and loading (ETL).Create and develop algorithms for machine learning and deep learning (CNN, RNN, LSTM, and BERT) for use in various applications.Integrated AI-driven features seamlessly into existing customer service systems, such as chatbots and CRM platforms, to improve efficiency and responsiveness.Worked with complex ETL pipelines on AWS (EMR, Lambda, Glue, Kinesis, Athena, Redshift) to create a completely automated log monitoring solution.Perform HR data collection and a variety of statistical analyses using Microsoft Excel, SAS, Tableau and Python.LLM-based models were created in order to identify possible problems early on, such as anomalies or abnormalities in operational logs or customer service interactions.Worked with a variety of machine learning techniques extensively using NumPy, Seaborn, Matplotlib, Spacy, Scikit-learn, SciPy, Pytorch, TensorFlow, and NLTK.C-suite executives received insightful reports and data visualizations that were created and presented using Tableau.Conducted in-depth market research using NLP techniques on customer feedback, influencing product roadmap decisions.Implemented a real-time fraud detection system, reducing fraudulent transactions by 40% within the first year of deploymentPresented findings at 5 industry conferences, showcasing innovative data-driven approaches and receiving 2 awards for excellence in data science.Trigent Software Jan 2018 - Aug 2020Data AnalystApplied Agile methodologies as a Data Scientist, collaborating cross-functionally to iteratively develop and deploy machine learning models, ensuring adaptability to evolving project needs.Designed and deployed interactive dashboards and reports using BI tools like Tableau, Power BI, to facilitate data-driven decision-making.Used complex statistical methods, Python, and SQL to analyze massive datasets and extract useful information for company enhancement.Employed suitable machine learning algorithms for data challenges, discerning between supervised and unsupervised learning techniques to optimize model performance and accuracy.Utilized expertise in statistical modeling with Python or R, including clustering, regression, time series analysis, and Bayesian statistics, to develop robust predictive models addressing various business challenges.Worked in tandem with cross-functional groups to comprehend business needs and convert them into data-driven solutions.Oversaw and cleaned datasets with up to 10 million records, making sure the information was accurate and complete.Applied A/B testing to marketing initiatives, which raised click-through rates by 15%.By analyzing customer feedback using natural language processing (NLP), a 30% decrease in product complaints was achieved.Implemented PySpark and Spark SQL workflows for filtering and aggregation on large-scale datasets in Hadoop clusters using Spark on GCP Dataproc, and managed job execution for data processing.Sliced and diced raw data for data cleansing utilizing pivot tables, Power Query, and Excel VLOOKUPs; this led to a 30% decrease in inaccuracy.Developed unsupervised methods for text embedding evaluation, including k-means, gaussian mixture models, and encoder-decoder architecture.Using DAX and Power Query, Created Excel and Tableau reports to analyze marketing data and produce predictive analytics insights on over 20 KPIs relevant to the topic of interest.Created global and personalized real time reports system for executives stakeholders and processes in SAS, Tableau, and proprietary systems.Using programs like Tableau and Matplotlib, clearly illustrated the findings to stakeholders.Conducted regular performance tuning and optimization of algorithms, improving model accuracy by 10%. Mentored junior data scientists in Python coding best practices and machine learning techniques.Contributed to the development of a customer segmentation strategy, resulting in a 12% increase in targeted marketing effectiveness.EDUCATION:Master of Science in Business Analytics (MS-BA)W. P. Carey School of Business at Arizona State University, Tempe, AZ Aug 2022 May 2023Bachelor of Technology in CosmeticsSant Gadge Baba Amravati University Jul 2012 May 2016 |
