DAVID GONZALEZ

Contact: 470-***-****; Email: ady4on@r.postjobfree.com

SENIOR DATA SCIENTIST / GENERATIVE AI ENGINEER

EXECUTIVE SUMMARY

Experienced Data Scientist with 8+ years of expertise driving innovation at the intersection of Data Science, Machine Learning, MLOps, Deep Learning, Computer Vision, NLP, Generative AI, and cloud technologies. Proven track record of delivering impactful projects across diverse industries, leveraging cutting-edge tools and methodologies. Adept at harnessing the power of large language models and cloud platforms such as AWS, Azure, and GCP to drive data-driven solutions. Accomplished team leader and collaborator, consistently achieving remarkable results.

Key highlights include:

Extensive proficiency in Data Science, Machine Learning, and MLOps, leading projects from concept to deployment.

Strong background in Deep Learning, specializing in Computer Vision and Natural Language Processing applications.

Pioneered Generative AI projects using large language models, driving innovation in content creation and more.

Leveraged cloud technologies such as AWS, Azure, and GCP to build scalable and efficient solutions.

Led cross-functional teams in designing and executing complex projects, ensuring successful outcomes.

Collaborated closely with stakeholders to understand business needs, translating them into actionable data strategies.

Excelled in harnessing cutting-edge tools and methodologies to derive valuable insights from complex data.

Consistently demonstrated a commitment to continuous learning and growth, staying current with evolving technologies.

A visionary professional, I bring a blend of technical expertise, leadership acumen, and a passion for innovation to drive business success through data-driven solutions.

Performance Milestones:

Spearheaded and successfully delivered a portfolio of complex projects spanning Data Science, Machine Learning, and MLOps, resulting in enhanced operational efficiency and informed decision-making.

Leveraged state-of-the-art Deep Learning techniques, including Computer Vision and NLP, to develop innovative solutions that transformed raw data into valuable insights, contributing to optimized processes and outcomes.

Led the adoption and implementation of cutting-edge Generative AI techniques using large language models, revolutionizing content creation and interaction within diverse industries, and achieving significant business growth.

PROFESSIONAL EXPERIENCE

Since July 2021 with Kmart Retail Remote from Atlanta Georgia

As a Senior Generative AI Engineer

Project 1: AI-Powered Generative Product Description Engine for Kmart Retail

As the lead for AI initiatives at Kmart Retail, I spearheaded a cutting-edge Generative AI project focused on revolutionizing customer engagement through AI-generated product descriptions. Leveraging the latest advancements in Natural Language Processing (NLP) and Generative AI, this initiative aimed to enhance Kmart's online shopping experience by providing dynamic and engaging product descriptions.

Key Responsibilities:

Orchestrated the end-to-end development of a Generative AI solution capable of crafting compelling and contextually relevant product descriptions.

Collaborated closely with stakeholders to understand the business need for dynamic and personalized content.

Led a multidisciplinary team comprising Data Scientists, NLP Experts, and Developers to conceptualize, prototype, and deploy the AI solution.

Implemented a generative model using OpenAI's GPT-3, fine-tuning it to cater specifically to Kmart's product domain and brand voice.

Designed and engineered efficient data pipelines to feed product information and attributes into the AI model for description generation.

Crafted a custom evaluation mechanism to assess the coherence, relevance, and linguistic quality of the AI-generated descriptions.

Orchestrated the integration of the Generative AI engine with Kmart's online platform, enabling seamless real-time content generation.

Collaborated closely with UX/UI designers to create an intuitive interface allowing content review and manual fine-tuning of AI-generated descriptions.

Conducted periodic model updates and refinements to continually enhance the quality and relevance of the generated content.

Achievements:

Successfully deployed the Generative AI-powered product description engine on Azure and AWS cloud platforms, reducing manual content creation efforts by 30%.

Transformed the online shopping experience by providing shoppers with dynamic and engaging product narratives, resulting in a 15% increase in conversion rates.

Led the team to receive the "Innovation in Customer Engagement" award for leveraging Generative AI to create personalized and relevant product descriptions.

Presented the project's success at industry conferences, showcasing Kmart's pioneering approach to leveraging Generative AI in the retail domain.

Technologies Used:

Cloud Platforms: Azure, AWS

Generative AI Models: OpenAI GPT-3 (fine-tuned)

Data Processing: Advanced NLP techniques, data preprocessing pipelines

Integration: Seamless integration with e-commerce platform

Evaluation: Custom evaluation metric for content quality and coherence

Collaboration: Agile methodologies (Jira), communication tools (Slack)

Project 2: Enhancing Customer Experience through Sentiment Analysis of Customer Reviews using Large Language Models

As the lead AI strategist at Kmart Retail, I led a transformative project aimed at revolutionizing our customer experience strategy by leveraging the power of Large Language Models (LLMs) to analyse and extract insights from customer reviews. The objective was to gain a deep understanding of customer sentiments, preferences, and pain points, enabling us to make data-driven decisions and drive continuous improvement.

Key Responsibilities:

Received and analyzed customer reviews data across various platforms, identifying trends, sentiments, and common issues.

Collaborated with Data Scientists, NLP Specialists, and ML Engineers to design and implement a sentiment analysis solution powered by Large Language Models.

Developed a custom data preprocessing pipeline to clean, tokenize, and prepare the text data for LLM-based analysis.

Integrated LLMs such as GPT-3 and BERT into the solution for robust sentiment classification and aspect extraction.

Led the development of a user-friendly dashboard that provided real-time insights and visualizations of customer sentiments.

Collaborated with the marketing and customer service teams to ensure actionable insights were utilized to enhance customer experience.

Monitored and fine-tuned the LLM models to improve accuracy and adapt to changing customer sentiment patterns.

Achievements:

Successfully deployed the sentiment analysis solution, resulting in a 20% reduction in negative reviews within the first quarter of implementation.

Enabled faster identification of emerging trends and customer preferences, leading to more targeted product offerings and improved inventory management.

Received recognition for contributing to a 15% increase in customer satisfaction scores, as reported in post-purchase surveys.

Presented the project's success to the executive team, highlighting its impact on enhancing customer-centric decision-making.

Technologies Used:

Large Language Models: OpenAI's GPT-3, BERT

Data Processing: Python (NLTK, SpaCy), Pandas, Numpy

Dashboarding: Tableau, Power BI

Cloud Platforms: AWS (S3, SageMaker), Azure (Blob Storage)

Collaboration: Agile methodologies (Jira), communication tools (Slack, GitHub)

Model Deployment: Docker, Kubernetes

Feb 2020 – Jul 2021 with Pfizer Pharmaceuticals Buffalo, NY (Remote from Atlanta)

As a Senior Data Scientist

Project 1: Early Disease Detection using Deep Learning for Pfizer Pharmaceuticals

As the Lead Data Scientist at Pfizer Pharmaceuticals, I led a transformative project focused on developing an AI-powered early disease detection system. Leveraging deep learning techniques, the project aimed to revolutionize patient care by identifying subtle disease indicators from medical images, thus enabling early intervention and improved treatment outcomes.

Key Responsibilities:

Collaborated with medical imaging experts and radiologists to define target diseases and the required dataset.

Curated and preprocessed a diverse dataset of medical images (X-rays, MRIs) from multiple sources.

Implemented Convolutional Neural Networks (CNNs) for feature extraction and disease classification.

Fine-tuned pre-trained CNN models (such as ResNet, VGG) using transfer learning to improve convergence and performance.

Employed data augmentation techniques to enhance model generalization and reduce overfitting.

Developed an interpretable heat map overlay to highlight disease-specific regions in medical images for radiologist review.

Collaborated with the IT team to deploy the deep learning model in Pfizer's secure infrastructure for seamless integration into the diagnostic workflow.

Achievements:

Successfully developed a deep learning model capable of accurately detecting target diseases with an average accuracy of 92%.

Significantly reduced the time-to-diagnosis by enabling radiologists to focus on regions of interest highlighted by the model's heat maps.

Contributed to Pfizer's commitment to innovation and patient care, earning recognition from industry peers and stakeholders.

Presented the project outcomes to Pfizer's executive leadership, showcasing the potential to revolutionize disease diagnosis and patient outcomes.

Technologies Used:

Deep Learning: Python (TensorFlow, Keras)

Image Processing: OpenCV

Data Manipulation: Pandas, Numpy

Visualization: Matplotlib, Seaborn

Collaboration: Jira, Confluence

Jun 2017 - Jan 2019 with BlackRock Financials Fort Washington, PA

As a Senior Consultant for AI

Project 1: Portfolio Risk Management using Machine Learning at BlackRock Financial

As a Senior Machine Learning Engineer at BlackRock Financial, I spearheaded a critical project aimed at enhancing the company's portfolio risk management strategies. Leveraging advanced machine learning techniques, the project focused on predicting and mitigating potential risks associated with various investment portfolios, ultimately optimizing investment decisions and improving client satisfaction.

Key Responsibilities:

Collaborated with portfolio managers and financial experts to define risk factors and data sources crucial for accurate risk prediction.

Curated and preprocessed a comprehensive dataset of historical market data, economic indicators, and portfolio compositions.

Engineered features that captured market volatility, asset correlations, and macroeconomic trends.

Employed machine learning algorithms, including Gradient Boosting and Support Vector Machines, to predict potential portfolio risks and deviations.

Developed an interactive web-based dashboard that provided real-time risk assessments and stress testing for various investment portfolios.

Collaborated with the Quantitative Research team to integrate the risk prediction model into BlackRock's proprietary trading platform.

Achievements:

Successfully reduced portfolio risk by 15% on average across multiple investment strategies, leading to improved client returns and confidence.

Earned recognition from BlackRock's senior management for pioneering innovative machine learning solutions that positively impacted investment strategies.

Presented project findings at industry conferences, showcasing BlackRock's commitment to cutting-edge technology and advanced risk management practices.

Technologies Used:

Machine Learning: Python (Scikit-learn, XGBoost)

Data Manipulation: Pandas, Numpy

Visualization: Matplotlib, Seaborn

Web Development: Flask, HTML/CSS, JavaScript

Database: SQLite

Collaboration: Jira, Confluence

Project 2: Fraud Detection using Ensemble Learning at BlackRock Financial

As a Lead Data Scientist at BlackRock Financial, I led a groundbreaking project focused on enhancing the company's fraud detection capabilities. Utilizing ensemble learning techniques, the project aimed to proactively identify fraudulent financial transactions within the organization's extensive transaction dataset, thus safeguarding client assets and upholding the company's reputation.

Key Responsibilities:

Collaborated with fraud analysts and domain experts to understand transaction patterns and anomalies indicative of fraudulent activities.

Curated and preprocessed a large-scale dataset containing transaction histories, user behaviors, and account details.

Engineered features that captured transaction frequencies, transaction amounts, and user behavior trends.

Implemented ensemble learning algorithms, such as Random Forest and Gradient Boosting, to detect fraudulent transactions based on complex patterns.

Developed an automated alert system that triggered real-time notifications for potential fraudulent activities, enabling prompt investigation and mitigation.

Collaborated with BlackRock's IT security team to integrate the fraud detection model into the company's transaction processing pipeline.

Achievements:

Successfully reduced false positives by 20%, enhancing the efficiency of fraud detection investigations and minimizing disruption to legitimate transactions.

Recognized for significantly enhancing BlackRock's fraud detection capabilities, ensuring the security of client assets and bolstering the company's reputation in the financial industry.

Presented the project outcomes at internal forums, demonstrating the positive impact of advanced data science techniques on financial security.

Technologies Used:

Machine Learning: Python (Scikit-learn, XGBoost, Random Forest)

Data Manipulation: Pandas, Numpy

Visualization: Matplotlib, Seaborn

Collaboration: Jira, Confluence

Jun 2015 - Jun 2017 with Twitter Miami, FL

As a MLOps Engineer

Enhancing ML Model Deployment and Monitoring at Twitter

As a Machine Learning Operations (MLOps) Engineer at Twitter, I led a pivotal project aimed at optimizing the deployment and monitoring of machine learning models across the platform. By implementing robust MLOps practices, this project focused on improving model reliability, scalability, and real-time monitoring, resulting in enhanced user experiences and data-driven decision-making.

Key Responsibilities:

Collaborated with cross-functional teams of data scientists, engineers, and DevOps specialists to understand model requirements and deployment needs.

Designed and implemented a streamlined CI/CD pipeline for model deployment using tools like Jenkins and Docker.

Leveraged Kubernetes for container orchestration, enabling seamless scaling and efficient resource utilization of deployed models.

Integrated Prometheus and Grafana for real-time model monitoring and performance tracking, ensuring timely detection of anomalies and potential issues.

Developed automated testing scripts and quality assurance measures to validate model functionality in different deployment environments.

Orchestrated seamless transitions between model versions, minimizing service disruptions and maintaining continuous uptime.

Collaborated with security teams to implement robust authentication and authorization mechanisms for model APIs.

Achievements:

Successfully reduced deployment time for machine learning models by 40%, accelerating the process of delivering new features and improvements to Twitter's users.

Improved model reliability by proactively detecting anomalies and performance degradation through real-time monitoring, minimizing user impact and downtime.

Received commendation for implementing best practices in MLOps, enhancing collaboration between data science and engineering teams and promoting a culture of continuous improvement.

Presented project outcomes at internal conferences, highlighting the significance of MLOps in enabling efficient and reliable machine learning deployments.

Technologies Used:

DevOps Tools: Jenkins, Docker

Container Orchestration: Kubernetes

Monitoring: Prometheus, Grafana

Automation: Shell scripting, Python

Collaboration: Jira, Confluence

ACADEMIC CREDENTIALS

Master of Science in Computer Science - Instituto Politécnico Nacional

Bachelor of Science in Systems Engineering - Instituto Tecnológico de Tuxtla Gutiérrez (TecNM)

Data Science Specialties: Natural Language Processing, Machine Learning, Internet of Things (IoT) analytics, Social Analytics, Predictive Maintenance, Stochastic Analytics

Analytic Skills: Bayesian Analysis, Inference, Models, Regression Analysis, Linear models, Multivariate analysis, Stochastic Gradient Descent, Sampling methods, Forecasting, Segmentation, Clustering, Naïve Bayes Classification, Sentiment Analysis, Predictive Analytics

Analytic Tools: Classification and Regression Trees (CART), H2O, Docker, Support Vector Machine, Random Forest, Gradient Boosting Machine (GBM), TensorFlow, PCA, RNN, Linear and non-Linear Regression

Analytic Languages and Scripts: R, Python, HiveQL, Spark, Spark MLlib, Spark SQL, Hadoop, Scala, Impala, MapReduce

Languages: Python, R, Bash, Powershell, C++/C, SQL, SAS, HTML

Python Packages: Numpy, Pandas, Scikit-learn, Tensorflow, SciPy, Matplotlib, Seaborn, Plotly, NLTK, Scrapy, Gensim

Version Control: GitHub, Git, SVN

IDE: Jupyter Notebook, VS Code, Intellij IDEA, Spyder, Eclipse

Data Query: Azure, Google BigQuery, Amazon RedShift, Kinesis, EMR; HDFS, RDBMS, SQL, MangoDB, HBase, Cassandra and NoSQL, data warehouse, data lake and various SQL and NoSQL databases and data warehouses

Deep Learning: Machine Perception, Data Mining, Machine Learning algorithms, Neural Networks, TensorFlow, Keras

Soft Skills: Experienced with delivering presentations and technical reports; collaboration with stakeholders and cross-functional teams, and providing advice on how to leverage analytical insights. Developed analytical reports which directly address strategic goals

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